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In this paper, a comparative study has been done on the returns of four equity trading strategies viz. Momentum Strategy, Contrarian Strategy, Value Stocks and Growth stocks during recent recovery of the Indian equity market. The main finding is that the returns from Value Stocks have been highest amongst these four. In Finance literature, the returns from pre-defined rules of making investments in equity have been an interesting topic for research. Returns from different trading rules have been an important part of the study of finance literature. Although The EMH ( Efficient Market Hypothesis) neglects any unusual returns from trading strategies which are based on the past information related to stock prices and financials, many researchers have proved otherwise and shown abnormal returns by implementing simple trading rules. [ABSTRACT FROM AUTHOR]

This work develops machine learning (ML) predictive models on price signals for financial instruments and their integration into trading strategies. In general, ML models have been shown powerful when trained with large amounts of data. In practice, the time-series nature of financial datasets limits the effective amount of data available to train, validate and retrain models since special care must be taken not to include future data in any way. In this setting, we develop deep generative models to produce synthetic time-series data, enhancing the amount of data available for training predictive models. Synthetic data obtained this way replicates the distribution properties of real historical data, leads to better performance, and enables thorough validation of predictive models for price signals. We leverage machine-generated predictive signals on synthetic data to build trading strategies. We show consistent improvement leading up to profits in our simulations for commodities and forex exchange markets. [Display omitted] • Generation of synthetic data produced by deep generative models in market predictions and trading strategies. • Comparison between real financial signals and financial signals produced by deep generative models. • Improved performance of trading strategies. • Generation of synthetic data using cross-validation methodology for time series. • Simulation of trading strategies and market predictions using cross-validation methodology for time series to mimic the notion of real-time. [ABSTRACT FROM AUTHOR]

This study provides the first empirical evidence on calendar anomalies in various passion investment markets for diamonds, fine wines, stamps, and fine arts. With several statistical techniques [e.g., average analysis, Student's t -test, analysis of variance (ANOVA), Kruskal–Wallis, and Mann–Whitney tests] and a trading simulation model, the results show that the passion investment markets depend on the calendar anomalies, which challenges the efficient market hypothesis. However, calendar anomalies are not homogeneous across various passion markets. In general, Day-of-the-Week and Month-of-the-Year Effects are the most commonly detected calendar anomalies, and some prices generally follow the Halloween Effect. The markets for wine, diamonds, and art follow a seasonality. However, the Turn-of-the-Year and Turn-of-the-Month Effects are primarily absent in the passion markets. A simulation analysis of profitable trading strategies provided practical implications for exploiting opportunities arising from the calendar anomalies in the passion investment markets. [Display omitted] • This study explores calendar anomalies in passion investment markets using several statistical techniques. • The results show the presence of calendar anomalies in the passion investment markets. • The most detected calendar anomalies are the Day-of-the-Week and Month-of-the-Year effects. • Seasonality is observed in the markets for wine, diamonds, and art. • The Turn-of-the-Year and Turn-of-the-Month effects are primarily absent in passion markets. [ABSTRACT FROM AUTHOR]

Nowadays, financial markets are no longer only for “big players”. Also small investors and traders have strong presence. Each of them uses their own strategy and therefore there are countless strategies. Each trading strategy has an equity curve, which gives us very important inside of how good or bad this strategy is. Aim of this paper is to look at the equity curve gained from the one specific trading strategy and find out, if it is possible to improve it (improve the final return, recovery factor, sharp ratio etc.) by applying some most used technical indicators on it. In other words, will we gain better performance by trading the equity curve itself? The used strategy is built on the well known idea of trading the divergences and the rules are applied on various currency pairs. In the results there is comparison of raw equity curves (unmanaged) and managed equity curves of this trading strategy. [ABSTRACT FROM AUTHOR]

For any large player in financial markets, the impact of their trading activity represents a substantial proportion of transaction costs. This paper proposes a novel machine learning algorithm for predicting the price impact of order book events. Specifically, we introduce a prediction system based on ensembles of random forests (RFs). The system is trained and tested on depth-of-book data from the BATS and Chi-X exchanges and performance is benchmarked using ensembles of other popular regression algorithms including: linear regression, neural networks and support vector regression. The results show that recency-weighted ensembles of RFs produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks. Feature importance ranking is used to explore the significance of various market features on the price impact, finding them to be highly variable through time. Finally, a novel procedure for extracting the directional effects of features is proposed and used to explore the features most dominant in the price formation process. [ABSTRACT FROM PUBLISHER]

We investigate (1) whether the trajectory of the current-quarter earnings expectation path (defined by the signs of the forecast revision and the earnings surprise) provides information about future firm performance, and (2) the extent to which analysts and investors react to that information. Our results indicate that analysts underreact more to earnings information revealed by consistent-signal earnings expectation paths than to earnings information communicated by inconsistent-signal expectation paths. We also find that the current earnings expectation path provides incremental explanatory power for future abnormal returns, even after controlling for the sign and magnitude of the earnings surprise. Overall, our evidence is consistent with underreaction stemming from analysts' and investors' bias in processing the information in consistent-signal earnings expectation paths. [ABSTRACT FROM AUTHOR]

In Part III of this study, we apply the price dynamical model with big buyers and big sellers developed in Part I of this paper to the daily closing prices of the top 20 banking and real estate stocks listed in the Hong Kong Stock Exchange. The basic idea is to estimate the strength parameters of the big buyers and the big sellers in the model and make buy/sell decisions based on these parameter estimates. We propose two trading strategies: 1) Follow-the-Big-Buyer, which buys when big buyer begins to appear and there is no sign of big sellers, holds the stock as long as the big buyer is still there, and sells the stock once the big buyer disappears; and 2) Ride-the-Mood, which buys as soon as the big buyer strength begins to surpass the big seller strength and sells the stock once the opposite happens. Based on the testing over 245 two-year intervals uniformly distributed across the seven years from July 3, 2007 to July 2, 2014, which includes a variety of scenarios, the net profits would increase 67% or 120% on average if an investor switched from the benchmark Buy-and-Hold strategy to the Follow-the-Big-Buyer or Ride-the-Mood strategies during this period, respectively. [ABSTRACT FROM PUBLISHER]

This paper analyzes spatial arbitrage and vertical integration of a U.S. soybean-trading firm. A risk-constrained optimization model using Monte Carlo simulation and copula joint distributions is specified. Results show that spatial-arbitrage payoffs vary regionally. Sensitivity results indicate that payoffs and risks increase as firms become more vertically integrated. [ABSTRACT FROM AUTHOR]

This paper proposes a modified version of the widely used price and moving average cross-over trading strategies. The suggested approach (presented in its ‘long only’ version) is a combination of cross-over ‘buy’ signals and a dynamic threshold value which acts as a dynamic trailing stop. The trading behaviour and performance from this modified strategy are different from the standard approach with results showing that, on average, the proposed modification increases the cumulative return and the Sharpe ratio of the investor while exhibiting smaller maximum drawdown and smaller drawdown duration than the standard strategy. [ABSTRACT FROM AUTHOR]

In this article, we propose a new hypothesis: that the efficient market hypothesis is day-of-the-week-dependent. We apply the test to firms belonging to the banking sector and listed on the NYSE. We find significant evidence that the efficient market hypothesis is day-of-the-week-dependent. Overall, for only 62% of firms, the unit root null hypothesis is rejected on all the five trading days. We also discover that when investors do not account for unit root properties in devising trading strategies, they obtain spurious profits. [ABSTRACT FROM AUTHOR]

We present the derivation of the self-financing condition used in a derivative pricing framework with funding, collateral and discounting. This is done in a way that clarifies the structure of the relevant funding accounts. This clarification is achieved by properly distinguishing between price processes, dividend processes and gains processes. Without this explicit distinction, the resulting self-financing condition can be erroneous, as we illustrate in the case of two papers: Piterbarg (2010) and Burgard & Kjaer (2011a). In these papers, the self-financing condition is equivalent to assuming that a subportfolio is self-financing on its own and without including the cash position. We show that the final result in Piterbarg (2010) is correct, even if the related self-financing condition is not. In the process, we raise a further question on the supplementary source of randomness in the funding rate dynamics that has no hedging counterpart in the replicating portfolio. [ABSTRACT FROM AUTHOR]

We consider the problem of measuring the performance of a dynamic strategy, re-balanced at a discrete set of dates, with the objective of hedging a claim in an incomplete market driven by a general multi-dimensional affine process. The main purpose of the paper is to propose a method to efficiently compute the expected value and variance of the hedging error of the strategy. Representing the payoff of the claim as an inverse Laplace transform, we are able to obtain semi-explicit formulas for strategies satisfying a certain property. The result is quite general and can be applied to a very rich class of models and strategies, including Delta hedging. We provide illustrations for the case of the Heston stochastic volatility model. [ABSTRACT FROM AUTHOR]

Different rating and investment companies have recently pointed out Spain’s brightening growth outlook, which has energized the Spanish stock market. By anticipating greater interest in the behaviour of the Spanish stock market, we show that the best trading strategy is that in which the investor enters long or short after the opening of the New York Stock Exchange (NYSE) till the end of the trading day at 17:30. This strategy should be complemented with that of entering long or short from the opening of the trading day till the closing price before the opening of the NYSE in no-coincidence phases. [ABSTRACT FROM PUBLISHER]

In this paper we study priming of identity within the context of inherent vs. contextual financial decision making. We use a sample of individual trading accounts in equity-style funds taken from one fund family to test the hypothesis that trading styles are inherent vs. contextual. Our sample contains investors who invest either in a growth fund, a value fund, or both. We document behavioral differences between growth fund investors and value fund investors. We find that their trades depend on past returns in different ways: growth fund investors tend towards momentum trading and value fund investors tend towards contrarian trading. These differences may be due to inherent clientele characteristics, including beliefs about market prices, specific personality traits and cognitive strategies that cause them to self-select into one or the other style. We use a sample of investors that trade in both types of funds to test this proposition. Consistent with the contextual hypothesis, we find that investors who hold both types of funds trade growth fund shares differently than value fund shares. [ABSTRACT FROM AUTHOR]

This paper uses new and recently established methodologies to study the evolutionary dynamics of the cryptocurrency market, and compares the findings with that of the equity market. We begin by applying random matrix theory and principal components analysis (PCA) to correlation matrices of both collections, highlighting clear differences in the eigenspectra exhibited. We then explore the heterogeneity of both asset classes, studying the time-varying dynamics of underlying sector behaviours, and determine the collective similarity within each collection. We then turn to a study of structural break dynamics and evolutionary power spectra, where we quantify the collective affinity in structural breaks and evolutionary behaviours of underlying sector time series. Finally, we implement two algorithms simulating 'portfolio choice' dynamics to compare the effectiveness of stock selection and sector allocation in cryptocurrency portfolios. There, we highlight the importance of both endeavours and comment on noteworthy implications for cryptocurrency portfolio management. • This paper uses time-varying random matrix theory to contrast cryptocurrency and equity dynamics. • Cryptocurrency and equity sector dynamics are compared. • Structural break dynamics are studied with a new framework. • A method to study collective similarity in evolutionary power spectra is introduced. • Two new algorithms test the effectiveness of cryptocurrency security selection and sector allocation. [ABSTRACT FROM AUTHOR]

The liberalization of energy markets brought full competition to the electric power industry. In the wholesale sector, producers and retailers submit bids to day-ahead markets, where prices are uncertain, or alternatively, they sign bilateral contracts to hedge against pool price volatility. In the retail sector, retailers compete to sign bilateral contracts with end-use customers. Typically, such contracts are subject to a high-risk premium—that is, retailers request a high premium to consumers to cover their potential risk of trading energy in wholesale markets. Accordingly, consumers pay a price for energy typically higher than the wholesale market price. This article addresses the optimization of the portfolios of retailers, which are composed of end-use customers. To this end, it makes use of a risk-return optimization model based on the Markowitz theory. The article presents a simulation-based study conducted with the help of the MATREM system, involving 6 retailer agents, with different risk preferences, and 312 real-world consumers. The retailers select a pricing strategy and compute a tariff to offer to target consumers, optimize their portfolio of consumers using data from the Iberian market, sign bilateral contracts with consumers, and compute their target return during contract duration. The results support the conclusion that retail markets are more favourable to risk-seeking retailers, since substantial variations in return lead to small variations in risk. However, for a given target return, risk-averse retailers consider lower risk portfolios, meaning that they may obtain higher returns in both favourable and unfavourable situations. • An agent-based simulation of the behaviour of retailers in retail electricity markets. • Risk-return optimization of consumers' portfolios based on retailers' risk attitude. • Optimization model for retailers, trading strategies, and forecast methodologies. • Description and simulation of a formal model of an agent-based retail competition. • A study testing retail competition involving real world consumers. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a broad picture and novel aspects of the financial crisis contagion with respect to the stages of crisis contagion and its propagation factors. We employ a pioneering approach to a simulation of the financial crisis contagion by embarking on a qualitative query rather than on empirical data (i.e. by adopting an international investor's perspective by conducting the qualitative query backed by semi-structured interviews with financial markets' participants). Building on modified Kaplan-Meier Survival Plots, we suggest a model for the financial crisis contagion based on international linkages between markets, with particular attention paid to spot vulnerabilities in regulatory frameworks that allowed for the crisis to spread. Simulation results showed that there were several phases of crisis contagion in Europe, and different countries (regions) were contained via different paths, propagated by different factors with not equal intensity. The diversity of European countries' susceptibility is evident not only when comparing advanced markets to the emerging ones, but also within these groups. Hereto, both international investment practitioners, as well as pan European market authorities should analyse with scrutiny the links emerging from the simulation, so that to develop sound and efficient investment strategies or impose tailor-made regulations for financial markets. [ABSTRACT FROM AUTHOR]

We develop a theory for the market impact of large trading orders, which we callmetaordersbecause they are typically split into small pieces and executed incrementally. Market impact is empirically observed to be a concave function of metaorder size, i.e. the impact per share of large metaorders is smaller than that of small metaorders. We formulate a stylized model of an algorithmic execution service and derive a fair pricing condition, which says that the average transaction price of the metaorder is equal to the price after trading is completed. We show that at equilibrium the distribution of trading volume adjusts to reflect information, and dictates the shape of the impact function. The resulting theory makes empirically testable predictions for the functional form of both the temporary and permanent components of market impact. Based on the commonly observed asymptotic distribution for the volume of large trades, it says that market impact should increase asymptotically roughly as the square root of metaorder size, with average permanent impact relaxing to about two-thirds of peak impact. [ABSTRACT FROM PUBLISHER]

Abstract: The Foreign Exchange Market is the biggest and one of the most liquid markets in the world. This market has always been one of the most challenging markets as far as short term prediction is concerned. Due to the chaotic, noisy, and non-stationary nature of the data, the majority of the research has been focused on daily, weekly, or even monthly prediction. The literature review revealed that there is a gap for intra-day market prediction. Identifying this gap, this paper introduces a prediction and decision making model based on Artificial Neural Networks (ANN) and Genetic Algorithms. The dataset utilized for this research comprises of 70 weeks of past currency rates of the 3 most traded currency pairs: GBP∖USD, EUR∖GBP, and EUR∖USD. The initial statistical tests confirmed with a significance of more than 95% that the daily FOREX currency rates time series are not randomly distributed. Another important result is that the proposed model achieved 72.5% prediction accuracy. Furthermore, implementing the optimal trading strategy, this model produced 23.3% Annualized Net Return. [Copyright &y& Elsevier]

This research proposes a new non-parametric approach to pairs trading based on renko and kagi constructions which originated from Japanese charting indicators and were introduced to academic studies by Pastukhov. The method exploits statistical information about the variability of the tradable process. The approach does not find a long-run mean of the process and trade towards it like other methods of pairs trading. The only assumption we need is that the statistical properties of the spread process volatility remain reasonably constant. The theoretical profitability of the method has been demonstrated for the Ornstein–Uhlenbeck process. Tests on the daily market data of American and Australian stock exchanges show statistically significant average excess returns ranging from 1.4 to 3.6% per month and annualized Sharpe ratio from 1.5 to 3.4. [ABSTRACT FROM AUTHOR]

Highlights: [•] We investigate the relative importance of anomaly-based strategies from a Mean Variance standpoint. [•] We employ spanning methodologies in a classical unconditional setting and a novel conditional setting. [•] In the classical setting the majority of the anomalies is needed to maximize return to risk compensation. [•] In the conditional setting the same maximization is obtained by including only two of the ten anomalies. [•] The efficient use of conditioning information reduces diversification benefits across anomalies. [Copyright &y& Elsevier]

We develop market timing strategies and trading systems to test the intra-day predictive power of Japanese candlesticks at the 5-minute interval on the 30 constituents of the DJIA index. Around a third of the candlestick rules outperform the buy-and-hold strategy at the conservative Bonferroni level. After adjusting for trading costs, however, just a few rules remain profitable. When we correct for data snooping by applying the SSPA test on double-or-out market timing strategies, no single candlestick rule beats the buy-and-hold strategy after transaction costs. We also design fully automated trading systems by combining the best-performing candlestick rules. No evidence of out-performance is found after transaction costs. Although Japanese candlesticks can somewhat predict intra-day returns on large US caps, we show that such predictive power is too limited for active portfolio management to outperform the buy-and-hold strategy when luck, risk, and trading costs are correctly measured. [ABSTRACT FROM AUTHOR]

We present an agent-based simulation of an asset market with heterogeneously informed agents. Genetic programming is applied to optimize the agents' trading strategies. After optimization, insiders are the only agents able to generate small systematic above-average returns. For all other agents, genetic programming finds a rich variety of trading strategies that are predominantly based on exclusive subsets of their information. This limits their price impact and prevents them from making systematic losses. The resulting low noise renders market prices as largely informationally efficient. [ABSTRACT FROM AUTHOR]

Abstract: Quantitative market timing strategies have been traditionally tested in liquid commodity and financial futures, often with mixed results with respect to their performance. We extend this methodology to a non-storable commodity, freight, where hitherto this analysis has not been carried out. The freight futures market is mature and increasingly liquid, making it a good case for diversification and trading opportunities. We carry out a comprehensive study of quantitative trading strategies in the FFA (Forward Freight Agreements) market on a wide variety of contracts and maturities with a number of trading rules. We find that in spite of robustness checks, trading rules do outperform the buy-and-hold benchmark in general. We also explore the possibility that illiquidity and a small sample size may impact the results of the tests and therefore offer an intuitive approach to mitigate their effects. A procedure that augments the Hansen (2005) SPA (Superior Predictive Ability) methodology and allows us to use it for smaller sample sizes with increased confidence is also proposed. [Copyright &y& Elsevier]

Abstract: We provide a mathematical framework to model continuous time trading of a small investor in limit order markets. We show how elementary strategies can be extended in a suitable way to general continuous time strategies containing orders with infinitely many different limit prices. The general limit buy order strategies are predictable processes with values in the set of nonincreasing demand functions. It turns out that our strategy set of limit and market orders is closed, but limit orders can turn into market orders when passing to the limit, and any element can be approximated by a sequence of elementary strategies. [Copyright &y& Elsevier]

It is shown that delta hedging provides the optimal trading strategy in terms of minimal required initial capital to replicate a given terminal payoff in a continuous-time Markovian context. This holds true in market models in which no equivalent local martingale measure exists but only a square-integrable market price of risk. A new probability measure is constructed, which takes the place of an equivalent local martingale measure. To ensure the existence of the delta hedge, sufficient conditions are derived for the necessary differentiability of expectations indexed over the initial market configuration. The phenomenon of 'bubbles,' which has recently been frequently discussed in the academic literature, is a special case of the setting in this paper. Several examples at the end illustrate the techniques described in this work. [ABSTRACT FROM AUTHOR]

Using virtual stock markets with artificial interacting software investors, aka agent-based models, we present a method to reverse engineer real-world financial time series. We model financial markets as made of a large number of interacting boundedly rational agents. By optimizing the similarity between the actual data and that generated by the reconstructed virtual stock market, we obtain parameters and strategies, which reveal some of the inner workings of the target stock market. We validate our approach by out-of-sample predictions of directional moves of the Nasdaq Composite Index. [ABSTRACT FROM AUTHOR]

We propose a framework for studying optimal market-making policies in a limit order book (LOB). The bid–ask spread of the LOB is modeled by a tick-valued continuous-time Markov chain. We consider a small agent who continuously submits limit buy/sell orders at best bid/ask quotes, and may also set limit orders at best bid (resp. ask) plus (resp. minus) a tick for obtaining execution order priority, which is a crucial issue in high-frequency trading. The agent faces an execution risk since her limit orders are executed only when they meet counterpart market orders. She is also subject to inventory risk due to price volatility when holding the risky asset. The agent can then also choose to trade with market orders, and therefore obtain immediate execution, but at a less favorable price. The objective of the market maker is to maximize her expected utility from revenue over a short-term horizon by a trade-off between limit and market orders, while controlling her inventory position. This is formulated as a mixed regime switching regular/impulse control problem that we characterize in terms of a quasi-variational system by dynamic programming methods. Calibration procedures are derived for estimating the transition matrix and intensity parameters for the spread and for Cox processes modelling the execution of limit orders. We provide an explicit backward splitting scheme for solving the problem and show how it can be reduced to a system of simple equations involving only the inventory and spread variables. Several computational tests are performed both on simulated and real data, and illustrate the impact and profit when considering execution priority in limit orders and market orders. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to focus on Initial Public Offering (IPO) investments, performance and activity in times of the global financial crisis. Design/methodology/approach – The paper utilizes, in a pioneering attempt, a modified regression model that is widely used in medical research (i.e. measuring the effectiveness of painkillers, aspects of breastfeeding, cancer research) but proved efficient and informative for the studied area. Embarking on Cox's Hazard Model perfectly mirrored investors' approach to IPO investments. Henceforth, the empirical findings reported in the paper became practical for IPO investors. The quantitative findings are then discussed with high-profile practitioners, in order to inject more realism into the study. The qualitative research framework expands the empirical analysis to cover significant issues related to IPO activities and proves invaluable in the process of constructing practical implications. Findings – Since the main purpose of the paper is to test the profitability of targeting IPOs from the Polish stock market, the main research question attempted in the paper refers to finding out whether IPO investments constitute an attractive alternative for direct equity investments, especially during the global financial turmoil. On this occasion, the current paper advises on trading strategies that involve targeting IPOs and shield investors from experiencing crisis-induced losses. These findings remain topical as they contribute to the current debate on tailoring investment approaches to the global financial crises. Furthermore, focusing on the issues related to the overblown deficit reported by the transition economy delivers novel and important implications for policymakers striving to stabilize budget in the aftermath of the nascent financial crisis. Originality/value – What distinguishes the paper from previous studies is the original methodology, three-dimensional approach to IPO activities (adopting a company's, investor's and policymaker's perspectives) and focusing on the systemically important European market that somehow was overlooked by previous studies in this area but recently vaulted into prominence among international investors who regard the Polish stock market as a regional leading bourse. [ABSTRACT FROM AUTHOR]