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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.

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.

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.

This paper focuses on forecasting the price of Bitcoin, motivated by its market growth and the recent interest of market participants and academics. We deploy six machine learning algorithms (e.g., Artificial Neural Network, Support Vector Machine, Random Forest, k-Nearest Neighbours, AdaBoost, Ridge regression), without deciding a priori which one is the ‘best’ model. The main contribution is to use these data analytics techniques with great caution in the parameterization, instead of classical parametric modelings (AR), to disentangle the non-stationary behavior of the data. As soon as Bitcoin is also used for diversification in portfolios, we need to investigate its interactions with stocks, bonds, foreign exchange, and commodities. We identify that other cryptocurrencies convey enough information to explain the daily variation of Bitcoin’s spot and futures prices. Forecasting results point to the segmentation of Bitcoin concerning alternative assets. Finally, trading strategies are implemented.

The vigorous development of Time Series Neural Network in recent years has brought many potential possibilities to the application of financial technology. This research proposes a stock trend prediction model that combines Gate Recurrent Unit and Attention mechanism. In the proposed framework, the model takes the daily opening price, closing price, highest price, lowest price and trading volume of stocks as input, and uses technical indicator transition prediction as a label to predict the possible rise and fall probability of future trading days. The research results show that the proposed model and labels designed by this research can effectively predict important stock price fluctuations and can be effectively applied to financial commodity trading strategies.

Undiversifiable (or systematic risk) has long been an enemy of investors. Many countercyclical strategies have been developed to counter this. However, like all insurance types, these strategies are generally costly to implement, and over time can significantly reduce portfolio returns in long and extended bull markets. In this paper, we discuss an alternative technique, founded on the premise of physiological bias and risk-aversion. We take a behavioral discussion in order to contextualize the insurance like characteristics of option pricing and discuss how this can lead to a mispricing of the asymmetric relationship between the VIX and the S&P 500. To test this, we perform studies in which we find statistical inefficiencies, thereby making it possible to implement a method of hedging index option premium in a way that has displayed no monthly drawdowns in bullish periods, while still providing large returns in major sell-offs. The three versions of the strategy discussed have negative betas to the S&P 500, while exhibiting similar risk-adjusted excess returns over both bull and bear markets. Further, the performance generated over the entire period, for all three strategies, is highly statistically significant. The results challenge the weak form of the Efficient Market Hypothesis and provide evidence that the methods of hedging could be a valuable addition to an equity rich portfolio for the purpose of counteracting systematic risk.

In the mutual fund literature, it is an established fact that investors “chase past performance”. However, the opposite impact of flows on performance is widely discussed. Mainly, liquidity costs are held responsible for short-term erosion of performance, while high inflows enhance performance over longer horizons. I investigate this relation for various groups of equity, bond, and money market funds and find significant outperformance in high inflow funds over several months, especially for specific bond fund groups. In addition, I test whether this information can be exploited using simple investment strategies but find that the abnormal returns are too low to offset associated costs.

This paper examines how the size of the rolling window, and the frequency used in moving average (MA) trading strategies, affects financial performance when risk is measured. We use the MA rule for market timing, that is, for when to buy stocks and when to shift to the risk-free rate. The important issue regarding the predictability of returns is assessed. It is found that performance improves, on average, when the rolling window is expanded and the data frequency is low. However, when the size of the rolling window reaches three years, the frequency loses its significance and all frequencies considered produce similar financial performance. Therefore, the results support stock returns predictability in the long run. The procedure takes account of the issues of variable persistence as we use only returns in the analysis. Therefore, we use the performance of MA rules as an instrument for testing returns predictability in financial stock markets.