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The value strategy and technical analysis strategy have existed in the financial market for a long time, and the impact of these two types of strategies on the financial market has also been debated for a long time. This paper studies the impact of trading strategies on market volatility by constructing a market ecology model including the simple technical strategy and value strategy. The results show that both the nature and the population size of a trading strategy can affect market volatility. In a market composed of the trend-following strategy and the value strategy, when the populations of the two strategies match, market volatility is low; when either of the two strategies has too much population, market volatility is high. However, in a market composed of the trend-reversal strategy and the value strategy, there is a positive correlation between market volatility and the population size of each strategy. The comparison of these results suggests that substantial diversification of trading strategies may be a fundamental force for market stability. [ABSTRACT FROM AUTHOR]

This study examines the predictability of the last 30 min of intraday stock price movements within the US financial market. The analysis encompasses several potential explanatory variables, including returns from each 30 min intraday trading session, overnight returns, the federal reserve fund rate decision days and the subsequent three days, the US dollar index, month effects, weekday effects, and market volatilities. Market-adaptive trading strategies are developed and backtested on the basis of the study's insights. Unlike the commonly employed multiple linear regression methods with Gaussian errors, this research utilizes a Bayesian linear regression model with Student-t error terms to more accurately capture the heavy tails characteristic of financial returns. A comparative analysis of these two approaches is conducted and the limitations inherent in the traditionally used method are discussed. Our main findings are based on data from 2007 to 2018. We observed that well-studied factors such as overnight effects and intraday momentum have diminished over time. Some other new factors were significant, such as lunchtime returns during boring days and the tug-of-war effect over the days after a federal fund rate change decision. Ultimately, we incorporate findings derived from data spanning 2022 to 2024 to provide a contemporary perspective on the examined components, followed by a discussion of the study's limitations. [ABSTRACT FROM AUTHOR]

Recently, a substantial body of literature in finance has implemented deep learning algorithms as predicting approaches. The principal merit of these methods is the ability to approximate any nonlinear and linear behaviors without understanding the data generation process, making them suitable for predicting stock market movement. This paper explores deep learning approaches to forecast stock price movement in the Hong Kong stock market. The forecasting performance of a temporal convolutional network (TCN) approach and several recurrent neural network (RNN) models is compared. The results show that the TCN can outperform all compared RNN models. Further parameter tuning results also show the superiority of the TCN approach. In addition, we demonstrate that a profitable strategy can be built based on the forecasting results of the proposed model. [ABSTRACT FROM AUTHOR]

Forecasting the stock market is a significant challenge in the financial industry due to its time series' complicated, noisy, chaotic, dynamic, volatile, and non-parametric nature. Nevertheless, due to computer advancements, an intelligent model can assist investors and expert analysts mitigate the risk associated with their investments. In recent years, substantial research has been conducted on deep learning models. Many studies have investigated using these techniques to anticipate stock values by analyzing historical data and technical indications. However, since the goal is to create predictions for the financial market, validating the model using profitability indicators and model performance is crucial. This article incorporates the attention mechanism model, incorporating attention from both feature and time perspectives. Utilize artificial neural networks. This approach addresses issues in time series prediction. The issue is the varying degrees of influence that many input features have on the target sequence. To tackle this, the method utilizes a feature attention mechanism to obtain the weights of distinct input features. An enhanced feature association relationship is achieved, whereas the data before and following the sequence exhibit a significant time correlation. An attention technique is employed to address this issue, allowing for the acquisition of weights at various time intervals to enhance robustness and temporal dependence. The system is applied to the three global SMs (TESLA, S&P500, and NASDAQ) datasets, the best enhancement results are 99% in Acc, and the better results improvement to minimize error in MSE, MAPE, and RMSE are 0.004, 0.004 and 0.01 respectively. [ABSTRACT FROM AUTHOR]

Devising a financial trading strategy that allows for long-term gains is a very common problem in finance. This paper aims to formulate a mathematically rigorous framework for the problem and compare and contrast the results obtained. The main approach considered is based on Dynamic Bayesian Networks (DBNs). Within the DBN setting, a long-term as well as a short-term trading strategy are considered and applied on twelve equities obtained from developed and developing markets. It is concluded that both the long-term and the medium-term strategies proposed in this paper outperform the benchmark buy-and-hold (B&H) trading strategy. Despite the clear advantages of the former trading strategies, the limitations of this model are discussed along with possible improvements.

Objective: To study the interactions among trading strategies and their profitability from an ecological perspective. Methods: A market ecosystem model is established, and simulations are conducted to examine the interactions and profitability of trading strategies in different market ecologies. Results: Strategies compete with themselves, and different time-window trend strategies exhibit competition and predator–prey relationships. Value and trend strategies demonstrate both symbiosis and predator–prey relationships. The profitability of a strategy depends on the balance of supporting and inhibiting effects, with greater supporting effects leading to higher maximum profit and market capacity, while greater inhibiting effects result in losses. The model suggests that fundamental analysis has a larger market capacity than technical analysis. [ABSTRACT FROM AUTHOR]

Devising a financial trading strategy that allows for long-term gains is a very common problem in finance. This paper aims to formulate a mathematically rigorous framework for the problem and compare and contrast the results obtained. The main approach considered is based on Dynamic Bayesian Networks (DBNs). Within the DBN setting, a long-term as well as a short-term trading strategy are considered and applied on twelve equities obtained from developed and developing markets. It is concluded that both the long-term and the medium-term strategies proposed in this paper outperform the benchmark buy-and-hold (B&H) trading strategy. Despite the clear advantages of the former trading strategies, the limitations of this model are discussed along with possible improvements. [ABSTRACT FROM AUTHOR]

In large-scale multiple hypothesis testing problems, the false discovery exceedance (FDX) provides a desirable alternative to the widely used false discovery rate (FDR) when the false discovery proportion (FDP) is highly variable. We develop an empirical Bayes approach to control the FDX. We show that, for independent hypotheses from a two-group model and dependent hypotheses from a Gaussian model fulfilling the exchangeability condition, an oracle decision rule based on ranking and thresholding the local false discovery rate (lfdr) is optimal in the sense that the power is maximized subject to the FDX constraint. We propose a data-driven FDX procedure that uses carefully designed computational shortcuts to emulate the oracle rule. We investigate the empirical performance of the proposed method using both simulated and real data and study the merits of FDX control through an application for identifying abnormal stock trading strategies. [ABSTRACT FROM AUTHOR]

The time proximity of high-frequency trades can contain a salient signal. In this paper, we propose a method to classify every trade, based on its proximity with other trades in the market within a short period of time, into five types. By means of a suitably defined normalized order imbalance associated to each type of trade, which we denote as conditional order imbalance (COI), we investigate the price impact of the decomposed trade flows. Our empirical findings indicate strong positive correlations between contemporaneous returns and COIs. In terms of predictability, we document that associations with future returns are positive for COIs of trades which are isolated from trades of stocks other than themselves, and negative otherwise. Furthermore, trading strategies which we develop using COIs achieve conspicuous returns and Sharpe ratios, in an extensive experimental setup on a universe of 457 stocks using daily data for a period of 4 years. [ABSTRACT FROM AUTHOR]

This research conducted a thorough investigation of Bitcoin volatility patterns using three interrelated methodologies: R/S investigation, simple moving average (SMA), and the relative strength index (RSI). The paper jointly employes the above techniques on volatility range-based estimators to effectively capture the unpredictable volatility patterns of Bitcoin. R/S analysis, SMA, and RSI calculations assess time series data obtained from our volatility estimators. Although Bitcoin is known for its high volatility and price instability, our analysis using R/S analysis and moving averages suggests the existence of underlying patterns. The estimated Hurst exponents for our volatility estimators indicate a level of persistence in these patterns, with some estimators displaying more persistence than others. This persistence underscores the potential of momentum-based trading strategies, reinforcing the expectation of additional price rises after declines and vice versa. However, significant volatility often interrupts this upward movement. The SMA analysis also demonstrates Bitcoin's susceptibility to external market forces. These observations indicate that traders and investors should modify their risk management approaches in accordance with market circumstances, perhaps integrating a combination of momentum-based and mean-reversion tactics to reduce the risks linked to Bitcoin's volatility. Furthermore, the existence of robust patterns, as demonstrated by our investigation, presents promising opportunities for investing in Bitcoin. [ABSTRACT FROM AUTHOR]

We find that insiders adopt dissimulation strategies to conceal their informational advantage and trade profitably when their firms' stock prices reach 52‐week highs and lows, exploiting the anchoring biases of uninformed investors. Insiders' trading profitability depends on their firms' future stock returns, operating efficiency, and investment sentiment, but not on earnings surprises. We document that male board members and insiders with long investment horizons are more likely to use dissimulation strategies. Overall, we provide evidence that insiders benefit from these price extremes, despite their status as publicly available, irrelevant, historical price levels that normally should not predict future stock returns. [ABSTRACT FROM AUTHOR]

We develop an approach for solving time‐consistent risk‐sensitive stochastic optimization problems using model‐free reinforcement learning (RL). Specifically, we assume agents assess the risk of a sequence of random variables using dynamic convex risk measures. We employ a time‐consistent dynamic programming principle to determine the value of a particular policy, and develop policy gradient update rules that aid in obtaining optimal policies. We further develop an actor–critic style algorithm using neural networks to optimize over policies. Finally, we demonstrate the performance and flexibility of our approach by applying it to three optimization problems: statistical arbitrage trading strategies, financial hedging, and obstacle avoidance robot control. [ABSTRACT FROM AUTHOR]

The energy markets are experiencing an enhanced volatility and unpredictability due to the growing integration of renewable energy sources in the grid and to the unstable geopolitical situation that is developing worldwide. Energy traders are therefore raising concerns on how to achieve solutions that not only ensure stability in terms of energy needs, both on the supply and demand side, but also enable profits within these markets. To cope with the complexity of this emerging scenario, tools that support traders in their decisions, such as algorithmic trading strategies, are attracting always more and more attention. In particular, evolutionary algorithms have emerged as an effective tool for developing robust and innovative trading strategies. Indeed, their flexibility and adaptability allow for the inclusion of various performance metrics. This article employs a recently issued evolutionary algorithm, called social network optimization, to identify the optimal closing criteria of already opened positions in an energy commodity market. More specifically, the proposed trading strategy is based on five self-defined parameters, which determine a profitable solution over nearly six years of available data. In particular, the overall average positive return achieved and the maximum monthly yield of 1.9% highlight the adaptability and robustness of the developed algorithmic trading strategy. Therefore, the results suggest the potentialities of developing and upgrading novel trading strategies by exploiting evolutionary computation techniques in the actual complex energy markets.

The rapid evolution of cryptocurrency markets and the increasing complexity of trading strategies necessitate a comprehensive understanding of price-prediction models and their direct impact on trading efficacy. While extensive research has been conducted separately on price prediction methods and trading strategies, there remains a significant gap in studies explicitly correlating precise price forecasts with successful trading outcomes. This review paper addresses this gap by critically examining the role of accurate cryptocurrency price predictions in enhancing trading strategies. We conducted a systematic review of sufficient scholarly articles and web resources, focusing on the methodologies and effectiveness of various predictive models and their integration into cryptocurrency trading strategies. Our selection criteria ensured the inclusion of papers that demonstrate methodological rigor, relevance, and recent contributions to the field, spanning from economic theories and statistical models to advanced machine learning techniques. The findings reveal that precise price predictions significantly contribute to the development of adaptive and risk-managed trading strategies, which are crucial in the highly volatile cryptocurrency market. The review also identifies current challenges and proposes directions for future research, emphasizing the need for interdisciplinary approaches and ethical considerations in predictive modeling. This synthesis aims to bridge the existing research gap and guide future studies, thereby fostering more sophisticated and profitable trading strategies in the cryptocurrency domain.

This study examines the predictability of the last 30 min of intraday stock price movements within the US financial market. The analysis encompasses several potential explanatory variables, including returns from each 30 min intraday trading session, overnight returns, the federal reserve fund rate decision days and the subsequent three days, the US dollar index, month effects, weekday effects, and market volatilities. Market-adaptive trading strategies are developed and backtested on the basis of the study’s insights. Unlike the commonly employed multiple linear regression methods with Gaussian errors, this research utilizes a Bayesian linear regression model with Student-t error terms to more accurately capture the heavy tails characteristic of financial returns. A comparative analysis of these two approaches is conducted and the limitations inherent in the traditionally used method are discussed. Our main findings are based on data from 2007 to 2018. We observed that well-studied factors such as overnight effects and intraday momentum have diminished over time. Some other new factors were significant, such as lunchtime returns during boring days and the tug-of-war effect over the days after a federal fund rate change decision. Ultimately, we incorporate findings derived from data spanning 2022 to 2024 to provide a contemporary perspective on the examined components, followed by a discussion of the study’s limitations.

We define a synthetic futures contract as a pair consisting of a terminal futures price J (a prespecified random variable) and a zero-value trading strategy whose terminal cumulative cash flow is equal to J to within an additive constant. The construction of synthetic futures contracts is demonstrated for (i) futures on futures, (ii) futures on spot, (iii) quanto futures on futures, (iv) quanto futures on spot and (v) futures on foreign futures and domestic futures. We formulate and derive the Law of One Futures Price, which justifies futures pricing based on such replication. [ABSTRACT FROM AUTHOR]

IntroductionThe cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors substantial profit opportunities, it also entails risks due to its sensitivity to speculative news and the erratic behavior of major investors, both of which can provoke unexpected price fluctuations.MethodsIn this study, we contend that extreme and sudden price changes and atypical patterns might compromise the performance of technical signals utilized as the basis for feature extraction in a machine learning-based trading system by either augmenting or diminishing the model's generalization capability. To address this issue, this research uses a bagged tree (BT) model to forecast the buy signal for the cryptocurrency market. To achieve this, traders must acquire knowledge about the cryptocurrency market and modify their strategies accordingly.Results and discussionTo make an informed decision, we depended on the most prevalently utilized oscillators, namely, the buy signal in the cryptocurrency market, comprising the Relative Strength Index (RSI), Bollinger Bands (BB), and the Moving Average Convergence/Divergence (MACD) indicator. Also, the research evaluates how accurately a model can predict the performance of different cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Binance Coin (BNB). Furthermore, the efficacy of the most popular machine learning model in precisely forecasting outcomes within the cryptocurrency market is examined. Notably, predicting buy signal values using a BT model provides promising results.

The purpose of the study is to highlight the key aspects necessary for the formation of a methodology for designing systems for managing trading operations over cryptocurrencies. The methodology of designing digital asset management systems defines a set of rules for using methods, models and algorithms required to build systems that solve the complex problem of managing trading operations over digital economy assets. For this purpose, the dynamics and trends of pricing of digital investment and financial assets are investigated to identify the peculiarities and specific patterns of cryptocurrency, taking into account the mathematical model of issuance in the conditions of a dynamic crypto-asset market, functioning continuously and forming cyclicality and wave structures.Materials and methods. The object of the study is the dynamics of value indicators of the market of digital financial assets and digital currencies. The subject of the study is methods, models and algorithms for automated monitoring and management of digital financial assets and cryptocurrencies. The methodological basis of the research is formed by blockchain technologies, modeling and mathematical statistics methods, artificial intelligence methods. The statistical information base of the research is formed on the basis of the history of Tradingview’s value quotations from international trading exchanges of digital currencies, as well as by Coinmarketcap and Coingecko cryptoasset market integrators. Problems of modeling processes in economic systems are considered, the problem of optimal control is defined. A critical analysis of the state in the tasks of economic modeling is carried out, taking into account the uncertainty caused by social and psychological reasons.Results. The proposed methodology offers a comprehensive solution to the problem of managing digital financial assets and other financial instruments based on blockchain technology. Digital currencies act as the management object of the proposed methodology, the initial information is a vector of parameters that determine the sensitivity of the system to the perturbing influences of the external environment and the requirements for the expected management results. In accordance with the scientific novelty of the research and methodology, a set of interrelated research stages is formed, consisting of an ordered cascade of methods, models and algorithms that perform preliminary analysis, processing and forecasting of financial time series of value indicators.Conclusion. New blockchain technologies and the emergence of Chat GPT (generative pre-trained transformer) pose new challenges to society, which hopes to utilize such solutions, including for economic tasks. With the help of prediction-free methods and artificial neural networks it is possible to design software systems, thanks to which it is possible to increase the efficiency of trading operations at optimal risks in automatic and automated mode of trade execution. The proposed methodology of management with auxiliary intellectual analysis of time series and application of combined method of decision-making allows managing the portfolio of a new asset class of digital currencies based on blockchain technologies. Taking into account the technical similarity of digital currencies with digital financial assets, it is possible to use the methodology also for the creation of digital financial asset management systems.

We introduce a continuous time model for stock prices in a general factor representation with the noise driven by a geometric Brownian motion process. We derive the theoretical hitting probability distribution for the long-until-barrier strategies and the conditions for statistical arbitrage. We optimize our statistical arbitrage strategies with respect to the expected discounted returns and the Sharpe ratio. Bootstrapping results show that the theoretical hitting probability distribution is a realistic representation of the empirical hitting probabilities. We test the empirical performance of the long-until-barrier strategies using US equities and demonstrate that our trading rules can generate statistical arbitrage profits. [ABSTRACT FROM AUTHOR]

Many recent papers have investigated the role played by volatility in determining the cross-section of currency returns. This paper employs two time-varying factor models: a threshold model and a Markov-switching model to price the excess returns from the currency carry trade. We show that the importance of volatility depends on whether the currency markets are unexpectedly volatile. Volatility innovations during relatively tranquil periods are largely unrewarded in the market, whereas during the unexpected volatile period, this risk has a substantial impact on currency returns. The empirical results show that the two time-varying factor models fit the data better and generate a smaller pricing error than the linear model, while the Markov-switching model outperforms the threshold factor models not only by generating lower pricing errors but also distinguishes two regimes endogenously and without any predetermined state variables. [ABSTRACT FROM AUTHOR]

The Capital Asset Pricing Model (CAPM) is a widely used and tractable model relating financial risk and return. However, it has been less successful when taken to the data. In their seminal paper, Frazzini and Pedersen propose a Betting Against Beta (BAB) factor to take advantage of this fact. In their construction, the BAB factor is a portfolio which longs low-beta stocks and shorts high-beta stocks. Since its construction, the BAB factor has been widely cited and used both in financial academics and in industry. This paper proposes a novel betting on and against beta (BOAB) strategy, which bets on beta on days with macroeconomic announcements and bets against beta on days without. Here, macroeconomic announcement days are defined as days when inflation, unemployment, or interest rate decisions are released. Our findings confirm the persistent positive beta-return relationship observed on macroeconomic announcement days. We show that compared to the BAB strategy, BOAB delivers higher average daily excess returns over the time period 1964 to 2021 when applied to the U.S. stock market. This outperformance of the BOAB strategy is robust to different constructions of the BAB factor and is economically and statistically significant when compared with the usual asset pricing factors such as value, size, and momentum. The outperformance of the BOAB strategy could be used to inform agents' portfolio allocation choices. Limitations are also discussed. Plain Language Summary: Macroeconomic announcements and trading strategies Trading strategies are important because they impact how financial market participants invest and manage risk-return tradeoffs. This paper proposes a betting on and against beta (BOAB) strategy, which takes long positions on beta on a subset of trading days and short positions on other days. We determined which days to take long beta positions by testing the prediction of asset pricing models on days with macroeconomic announcements. We carefully constructed macroeconomic announcement days and used the Fama-MacBeth two-step testing procedure to evaluate predictions of the CAPM model. We conclude that the BOAB strategy outperforms the BAB factor, especially due to its outperformance on days with macroeconomic announcements. In the process, we consider varying ways of constructing the betting against beta factor to address critiques of the original BAB factor. We also demonstrate that the large, positive returns of the BAB factor has diminished since the publication of the original BAB paper. [ABSTRACT FROM AUTHOR]

This research conducted a thorough investigation of Bitcoin volatility patterns using three interrelated methodologies: R/S investigation, simple moving average (SMA), and the relative strength index (RSI). The paper jointly employes the above techniques on volatility range-based estimators to effectively capture the unpredictable volatility patterns of Bitcoin. R/S analysis, SMA, and RSI calculations assess time series data obtained from our volatility estimators. Although Bitcoin is known for its high volatility and price instability, our analysis using R/S analysis and moving averages suggests the existence of underlying patterns. The estimated Hurst exponents for our volatility estimators indicate a level of persistence in these patterns, with some estimators displaying more persistence than others. This persistence underscores the potential of momentum-based trading strategies, reinforcing the expectation of additional price rises after declines and vice versa. However, significant volatility often interrupts this upward movement. The SMA analysis also demonstrates Bitcoin’s susceptibility to external market forces. These observations indicate that traders and investors should modify their risk management approaches in accordance with market circumstances, perhaps integrating a combination of momentum-based and mean-reversion tactics to reduce the risks linked to Bitcoin’s volatility. Furthermore, the existence of robust patterns, as demonstrated by our investigation, presents promising opportunities for investing in Bitcoin.

With the increase in rooftop photovoltaic (PV) systems at the residential level, customers owning such renewable resources can act as a source of generation for other consumers in the same network. Peer-to-peer (P2P) energy trading refers to a local trading platform where the residential customers having excess PV power (prosumers) can interact with their neighbors without PV resources (customers) to improve the social welfare of society. However, the performance of a P2P market depends on the power system network constraints and trading strategy adopted for local energy trading. In this paper, we compare different trading strategies, i.e., the rule-based zero intelligent (ZI) strategy and the preference-based game theory (GT) approaches, for a constrained P2P platform. Quadratic trading loss and impedance-based network utilization fee models are suggested to define the network constraints for the P2P system. Additionally, a reluctance-based prosumer-sensitive model is developed to adjust the trading behavior of the participants under the heavy distribution losses/network fee. The presented results show that the suggested trading strategies enhanced the average welfare of the participants by approximately 17%. On average, the customers saved about $33.77 monthly, whereas the average monthly earnings of the prosumers were around $28.3. The ZI strategy enhanced the average monetary advantages of all the market participants by an average of 7% for a system having small distribution losses and a network fee as compared to the GT approach. Contrarily, for a system having high losses/a utilization fee, the GT approach improved the average welfare of the prosumers by around 75% compared to the ZI strategy. However, both trading strategies yielded competitive results compared to the traditional market under the standard values of network coefficients. [ABSTRACT FROM AUTHOR]

This study forecasts the oil volatility index (OVX) incorporating information from other implied volatility (IV) indices. We provide evidence for the existence of long memory in the OVX in order to justify the use of the Heterogeneous AutoRegressive (HAR) model. We extend the HAR model by implementing a dynamic model averaging (DMA) method in order to allow for IV indices from other asset classes to be applicable at different time periods. Apart from the statistical evaluation, a straddle options trading strategy validates our results from an economic point of view. The IV of Dow Jones is highly significant for short- and mid-run forecasting horizons, whereas, at longer horizons, the IV of Energy Sector provides accurate forecasts but only from an economic point of view. [ABSTRACT FROM AUTHOR]

This paper explores the use of machine learning algorithms and narrative sentiments when applied to the task of forecasting and trading Bitcoin. The forecasting framework starts from the selection among 295 individual prediction models. Three machine learning approaches, namely, neural networks, support vector machines, and gradient boosting approach, are used to further improve the forecasting performance of individual models. By taking data‐snooping bias into account, three different metrics are applied to examine the forecasting ability of each model. Our results suggest that the machine learning techniques always outperform the best individual model whereas the gradient boosting framework has the best performance among all the models. Finally, a time‐varying leverage trading strategy combined with narrative sentiments and volatility is proposed to enhance trading performance. This suggests that the hybrid leverage strategy provides the highest Bitcoin profits consistently among all trading exercises. [ABSTRACT FROM AUTHOR]

In this paper, we examine the usefulness of machine learning methods such as support vector machines, random forests and bagging for the extraction of information from the limit order book that can be used for intraday trading. For our empirical analysis, we first get 50 raw features from the LOBSTER message file and order book file of the iShares Core S & P 500 ETF for the time period from 27.06.2007 to 30.04.2019 and then construct 18 higher-level features (aggregated to 5 minutes frequency) which serve as predictors. Using straightforward specifications for the machine learning procedures and thereby avoiding excessive data snooping, we find that these procedures are unable to find high dimensional patterns in the order book that could be used for trading purposes. The observed significant predictability is mainly due to the inclusion of only one variable, namely the last price change, and is probably too small to ensure profitability once transaction costs are taken into account.

In this paper, we apply an updated Coppock trading rule and four trading strategies to two energy ETFs, United States Oil (USO) and United States Gasoline Fund (UGA), using weekly data from 2006 to 2022. Our four trading strategies are designed for different levels of risk tolerance. Strategy 1 is for low risk tolerance investors, strategy 2 for medium risk tolerance investors, and strategies 3 and 4 are for high-risk tolerance investors. For each ETF, we compare the performance of buying and holding this ETF (B&H strategy) to the performance of our trading rule for that ETF. We find our trading rules significantly outperforms the B&H strategy. Traders with low, medium, and high-risk tolerance can use our trading rule with combination of four recommended strategies and obtain an improved risk-return tradeoff than the B&H strategy. Further, our results are robust when we apply our trading system to two equal sub-periods for each ETF. [ABSTRACT FROM AUTHOR]

In this study, we analyze the volatility of volatility indices and estimate the Hurst parameter using data from five international markets. For our analysis, we consider daily data from VIX (CBOE), VXN (CBOE Nasdaq 100), VXD (DJIA), VHSI (HSI), and KSVKOSPI (KOSPI). The period of analysis is from January 2001 to December 2021 and incorporates various market phases, such as booms and crashes. The novelty here is the use of recent methodology, including different range-based estimators for volatility analysis. We apply the Hurst exponent to the volatility measures Vgk,t, Vp,t, Vrs,t, and Vs,t, and then estimate the volatility of volatility indices through the GARCH(1, 1) model. Based on the values of the Hurst exponent, we analyze the trace of the behavior of three trading strategies, i.e., the momentum-based strategy, the random walk, and the mean-reversion strategy. The results are highly recommended for financial analysts dealing with volatility indices as well as for financial researchers. [ABSTRACT FROM AUTHOR]

As shipping market players operate in a competitive and volatile business environment, their highly capital-intensive investment decisions to target value-generating projects are exposed to critical risks. Therefore, the paper investigates the efficiency of investment strategies based on the combination of technical trading rules and fundamental analysis in selling and purchasing ships in the container shipping market. Using historical datasets of second-hand vessel prices and time-charter rates from October 1996 to June 2021, long-run cointegrating implications and short-run causality spillover effects are examined for three groups of containerships, distinguished by their transportation capacity, viz. 725 TEUs, 1700 TEUs, and 3500 TEUs. In addition, the moving average trading rules are used to indicate the timing of investment or divestment decisions through the analysis period. The results for vessel prices and earnings for 3500 TEU containerships appeared to be more volatile compared to smaller ships (725 TEUs and 1700 TEUs), while time-charter earnings are seen to exert an impact on second-hand prices across all vessel types. Moreover, due to higher volatility, the trading strategies based on price-earning ratios significantly outperform the buy-and-hold strategies for the 3500 TEU and the 1700 TEU containerships. On the contrary, the decision to buy-and-hold smaller container ships (725 TEU) yields higher profits than the active sale and purchase strategy. The insights provided in this paper can be used by multiple stakeholders, such as liner operators, investors, lessors, and researchers. [ABSTRACT FROM AUTHOR]

Objective: Investors which enter the capital market, seek to find ways and try to formulate and apply strategies that can win in the market and earn additional returns. This view is in direct opposition to the market efficiency hypothesis; Because the efficient market hypothesis states, there is no specific trend and pattern in the performance of securities prices, and the behavior of prices is random and unpredictable. According to the efficient market hypothesis, portfolio performance is independent of its past performance; But in the trading strategies of acceleration, one tries to predict the future performance by using the past performance and create additional returns. Based on the hypothesis of an efficient market, acceleration trading strategies are among the anomalies and exceptions of the capital market, which are used to take advantage of the serial correlation in the yield of securities. There are different types of momentum trading strategies. Profit acceleration and momentum strategies are two of the most widely used acceleration trading strategies. In this regard, the concept of deviation from financial leverage to the average is one of the factors that can affect this abnormality of the capital market. Deviation from the financial leverage to the average means that the changes in the financial leverage have a deviation towards the average regardless of the financing decisions and the existence of the target financial leverage. Deviation from financial leverage to the mean seems a surprising concept at first sight; But the explanation of this issue is very simple; Because there is a rationale for why the average debt ratio that is below the limit increases and those that are above it decrease, even with random financing decisions. The phenomenon of deviation from leverage to the mean is one of the factors affecting this anomaly When the company seeks to use more equity for financial provision, the ratio of financial leverage increases; Therefore, the company bears the cost of issuing shares and does not reach its goal, which increases the cost of the company's profit and, as a result, reduces the profit acceleration over time. Therefore, it seems that the deviation from the financial leverage to the average has a negative effect on the additional return of the profit acceleration strategy. On the other hand, increasing the percentage of debt in the capital structure increases the company's risk and the attractiveness of the company's shares decreases for investors, and as a result, the share price decreases, and this price decrease over time reduces the additional momentum return; Therefore, the concept of deviation from the financial leverage to the average has a negative effect on the excess return of momentum. The additional return of trading strategies including profit acceleration and momentum strategies means that stocks that have recently had a surprise in profit or stock price will continue to act in the same direction in the near future. In other words, a stock that has had a positive adjustment will have a good yield in the near future. Based on what was said, the company's financial leverage ratio, regardless of financing decisions, tends to the average and affects changes in profit and momentum; Therefore, in this research, by using the Monte Carlo simulation method and making random changes in the amount of debt and equity, the effect of the concept of deviation from financial leverage to the average on the additional return of profit acceleration and momentum strategies is investigated. Method: This research in terms of its purpose is a part of applied research, and in terms of methodology, it is a post-occurrence causal correlation research. In this research, to collect information in the field of literature and research background, the required information has been collected by reading books, articles and searching on internet sites. Also, in order to collect the necessary information to test the hypotheses, Rahvard Navin software, Stock Exchange website and Codal website were used. The additional return of acceleration trading strategies, including profit acceleration and momentum is measured by buying past winning stocks and selling past losing stocks, and the Bootstrap simulation method has been used to measure the variable of deviation from leverage to the mean. For this purpose, a sample consisting of 1430 companies listed on the Tehran Stock Exchange during the years 2008-2020 has been investigated using multivariate regression. Results: The results of the research showed that the deviation from leverage to the mean for 3- and 6-month strategies has a negative and significant relationship with profit acceleration and momentum; But for 12- and 24-months strategies, the deviation from the leverage to the mean does not affect on the profit acceleration and momentum. The negative and significant value of the variable coefficient of deviation from the financial leverage to the average for the period of 3 and 6 months means that there is an inverse and significant relationship between the increase in the deviation from the financial leverage to the average and the additional yield of profit acceleration in the time period of 3 and 6 months. This result shows that by increasing the financial leverage and moving away from the average (optimal financial leverage), the abnormal return rate around the profit announcement in the time horizon of 3 and 6 months is reduced, and vice versa, as the financial leverage decreases and approaches the optimal financial leverage, the return rate Abnormality around earnings announcement increases in 3 and 6 month's time horizon. In other words, there is a possibility of increasing the additional yield of profit acceleration in the Tehran Stock Exchange in the time horizon of 3 and 6 months by reducing the concept of deviation from financial leverage to the average. Conclusion: This result shows that the best strategy for investigating the effect of deviation from leverage to the mean on the additional return of profit acceleration and momentum is short-term and medium-term time strategies. One-year and two-year time strategies cannot explain the phenomenon of deviation from leverage to the mean due to the long-term effect of economic factors on profits and stock prices. Also, the phenomenon of deviation from leverage to the mean shortens the managers' hands to optimize the leverage and reduces the profit acceleration and momentum acceleration; Therefore, choosing the right optimal leverage and correcting it in a timely manner is an effective factor in continuing the trend of future earnings and returns of companies. [ABSTRACT FROM AUTHOR]

Purpose – The present research aims to examine a range of momentum trading strategies for the tourism and hospitality sector. Design/methodology/approach – The paper followed the methodology of Jegadeesh and Titman (1993) to construct the portfolios. In this methodology, all portfolios were formed and evaluated by their cumulative stock returns over the past J periods and holding the position for the next K periods. In total, nine formation and holding periods were used, represented by 3, 6 and 12. For example, strategy 3–3 (that is, strategy with J = 3 and K = 3) refers to the strategy that stocks are ranked based on their previous three months and then held for the next three months. Findings – The findings demonstrated that none of these momentum investing strategies was profitable. Most of the results, however, show positive, but insignificant momentum returns. This finding can be interpreted as price reversal over a horizon of three to twelve months in the US hospitality and tourism sector. These results are robust to size, different formation and holding combinations, beta and turnover. Research limitations/implications – Regarding the research limitations, this paper only considers the US tourism and hospitality sector. Therefore, the extension of results to other developed and developing markets should be taken carefully. Also, this paper relies only on the methodology of Jegadeesh and Titman (1993). Other methodologies could be suitable avenues for future research. Practical implications – Investors and portfolio managers who seek for earning abnormal returns by investing in the US HT stocks can attain their hopes by constructing portfolios based on existing guidelines in the literature and adopting a short-term reversal trading strategy or by buying past losers and selling past winners of the US tourism and hospitality stocks. Originality/value – This research contributes to the hospitality finance literature by offering the investors who are interested in the US hospitality and tourism sector an uncomplicated trading rule that uses real return data and is expected to generate actual returns. Moreover, the momentum strategy of Jegadeesh and Titman (1993) is never used in the hospitality finance literature. 研究目的 – 本研究旨在探討各種可應用於旅遊及酒店業的動量交易策略。 研究設計/方法/理念 – 本文按照 Jegadeesh 和 Titman（1993）的研究方法來建造投資組合。使用這研究方法時，所有投資組合均以它們在過去J 時期的累積股票收益和在未來K 時期的持倉來建立及評價的。九個組成方式及持有期被使用，以3、6、12來表示。例如，策略3-3（那就是說，該策略以J = 3和 K = 3）指的策略是以有關的股票基於過去三個月而被分等級，繼而在未來三個月被持有。 研究結果 – 研究結果顯示，這些投資策略全沒帶來利潤；唯大部分結果顯示正動能策略報酬，雖報酬是微不足道的。這研究結果或許可理解為在美國酒店及旅遊業為期三至十二個月的價格逆轉。這些結果就規模、不同組成方式和持有組合、beta 和成交量而言是強而有力的。 研究的局限/意義 – 就研究的局限而言，本文只是考慮美國的酒店及旅遊業；因此，如把研究結果伸延至其它已開發或發展中的市場，則需小心處理。另外，本文只依賴 Jegadeesh 和 Titman（1993）的研究方法，就此，使用其它研究方法會是日後相關研究的適當途徑。 實際的意義 – 欲透過投資於美國酒店及旅遊股票而尋求賺取異常收益的投資者和投資組合經理可如願以償，方法是基於文獻內現存的準則建造投資組合，以及採用短期的逆轉交易策略，或買入美國酒店及旅遊業過去輸家及賣出過去贏家。 研究的原創性/價值 – 本研究為酒店金融文獻作出貢獻，因研究為對美國酒店及旅遊業有興趣的投資者提供了使用實際收益數據及預期可創造實際回報的簡單交易規則；而且， Jegadeesh 和 Titman（1993）的動量策略從未在酒店金融文獻內被使用過。

It is common practice to employ returns, price differences or log returns for financial risk estimation and time series forecasting. In De Prado's 2018 book, it was argued that by using returns we lose memory of time series. In order to verify this statement, we examined the differences between fractional differencing and logarithmic transformations and their impact on data memory. We employed LSTM (long short-term memory) recurrent neural networks and an XGBoost regressor on the data using those transformations. We forecasted risk (volatility) and price value and compared the results of all models using original, unmodified prices. From the results, models showed that, on average, a logarithmic transformation achieved better volatility predictions in terms of mean squared error and accuracy. Logarithmic transformation was the most promising transformation in terms of profitability. Our results were controversial to Marco Lopez de Prado's suggestion, as we managed to achieve the most accurate volatility predictions in terms of mean squared error and accuracy using logarithmic transformation instead of fractional differencing. This transformation was also most promising in terms of profitability. [ABSTRACT FROM AUTHOR]

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