A hybrid decision tree-Markowitz framework for intelligent trading portfolio optimization

Despite active research on trading systems based on reinforcement learning, the development and performance of research methods require improvements. This study proposes a new action-specialized expert ensemble method consisting of action-specialized expert models designed specifically for each reinforcement learning action: buy, hold, and sell. Models are constructed by examining and defining different reward values that correlate with each action under specific conditions, and investment behavior is reflected with each expert model. To verify the performance of this technique, profits of the proposed system are compared to those of single trading and common ensemble systems. To verify robustness and account for the extension of discrete action space, we compared and analyzed changes in profits of the three actions to our model's results. Furthermore, we checked for sensitivity with three different reward functions: profit, Sharpe ratio, and Sortino ratio. All experiments were conducted with S&P500, Hang Seng Index, and Eurostoxx50 data. The model was 39.1% and 21.6% more efficient than single and common ensemble models, respectively. Considering the extended discrete action space, the 3-action space was extended to 11- and 21-action spaces, and the cumulative returns increased by 427.2% and 856.7%, respectively. Results on reward functions indicated that our models are well trained; results of the Sharpe and Sortino ratios were better than the implementation of profit only, as in the single-model cases. The Sortino ratio was slightly better than the Sharpe ratio.

Recently, researchers have been paying increasingly more attention to risk-adjusted performance of investment funds. Nevertheless, the asymmetry, the smoothness or the peakedness of returns have been poorly explored. In addition, most researches have been carried out in well developed, liquid and efficient markets.In this study, risk-adjusted performance of investment funds traded in the stock exchange of the Baltic countries was assessed using Sharpe and Sortino ratios as well as skewness and kurtosis. There are 38 investment funds traded in the stock exchange of the Baltic countries. However, most of these funds have been offered to public only recently. Thus, in order to have a research period as long as possible, only 14 funds were selected: 4 equity funds, 4 bond funds and 6 funds of funds. The period of investigation is 2008-2016.The research results revealed that only bond funds and one equity fund (ZPR Global Equity) were efficient in 2008-2016. This equity fund mostly invested in companies from the USA, where the economy, hit by the financial crisis, began to grow at a fast pace. All funds of funds were inefficient. This research was conducted in a relatively illiquid and inefficient market, but the investors’ behaviour during the economic downturn and slow growth period was the same as in efficient markets: they shifted their investments from equities to bonds. Consequently, additional demand increased bond market prices and returns.Despite the fact that only bond funds were efficient, their skewness and kurtosis were negative and greater in comparison with equity funds and funds of funds – the return distribution in bond had a greater downside. Moreover, bond funds were: 1) more overestimated by Sharpe ratio than equity funds and funds of funds; 2) their return distribution had a greater gain/loss than could be expected from a normal distribution; 3) their kurtosis were greatest in comparison with the kurtosis of equity funds and funds of funds. It should be emphasised that if the investor considered large negative skewness and very large positive excess kurtosis, he would come to a different conclusion. This is particularly important for intermediately and highly risk-averse investors.The analysis showed that almost all investment funds (with exception of one) had negative skewness (greater downside events), making the use of standard deviation and reliance upon normal distributions much less relevant for understanding the true risk of investment funds. 13 investment funds had leptokurtic return distributions, and 1 fund – platykurtic. Compared to a normal distribution, the latter tails are shorter and thinner. An asymmetrical return distribution and fat tails lead to the danger of underestimating risk and overestimating risk-adjusted performance with standard risk and performance measures. The correlation between the Sharpe and Sortino ratios is almost linear, which allows for concluding that investment funds can be equally ranked by both indicators. Contrarily, if some investment funds had negative and the others – positive skewness, the correlation would be lower (Cibulskiene et al., 2017) and these funds would be differently ranked by the Sharpe and Sortino ratios. This statement contradicts the results of Eling (2008), who found that investment funds could be equally ranked by different risk-adjusted performance indicators.

Portfolio choice with stochastic interest rates and learning about stock return predictability

Performance analysis of the integration between Portfolio Optimization and Technical Analysis strategies in the Brazilian stock market

ABSTRACTIn this article, we document the asymmetric role that the US stock market plays in the international predictability of excess stock returns during recession and expansion periods. Most of the positive evidence accrues during the periods of recessions in the United States. During the expansions, there is only a limited evidence supporting the importance of lagged US returns in predictability of stock returns in 10 industrialized countries.

A portfolio optimization problem involves optimal allocation of finite capital to a series of assets to achieve an acceptable trade-off between profit and risk in a given investment period. In the paper, the extended Markowitz's mean-variance portfolio optimization model is studied with some practical constraints. We introduce a new operator and an adaptive strategy for improving the performance of the multi-dimensional mapping algorithm (MDM) proposed specially for the portfolio optimization. Experimental results show that the modification is efficient on tackling large-scale portfolio problems.

The study proposes to include the financial analysis (FA) in optimal portfolio selection. The role of FA in investment decisions is well recognized. While comparing two stocks on FA of their companies it is important to have both drawn from the same sector of economy. This reason motivated us to propose a sectoral portfolio optimization (SPO) which, instead of looking to optimize among all stocks together, focuses on optimizing stocks within each sector on the basis of FA. These stocks are then pooled together and an optimal portfolio is formed from them with their FA weights and mean returns. In context of FA, the four financial ratios included in present study are return on asset (profitable ratio), debt-assets ratio (solvency ratio), current ratio (liquidity ratio), and price-to-earning ratio (valuation ratio). The risk in a portfolio is quantified using the second order stochastic dominance and to this effect constraints are added in the selection process to generate optimal portfolios for rational risk averse investors. The performance of the optimal portfolios from the proposed model is tested against the portfolios from the traditional second order stochastic dominance model [named (SSDP) in this work], the benchmark index and four 5-star rated mutual funds of India from diversified equity. The out-of-sample analysis is carried on mean returns, Sharpe ratio, Sortino ratio, and also their ability to dominate the benchmark index in almost second order stochastic dominance sense over the tolerable violation regions. The stock price data for the period April 2004 to November 2014 of S&P BSE 500 index is used for testing the models. The optimal portfolios generated from the SPO perform better than the portfolios generated from the (SSDP), the benchmark index and the MFs, indicating effectiveness of FA in SPO framework.

This chapter is a continuation and extension of modern portfolio theory presented in Chapter 3, with an emphasis on risk measures and risk management of a portfolio. It introduces the capital asset pricing model (CAPM), linear factor models, and several approaches to portfolio risk measures such as value-at-risk, conditional value-at-risk and the concept of coherent risk measures, as well as a variety of portfolio evaluation techniques such as the alpha and beta, the Sharpe ratio, the Sortino ratio and maximum drawdown. The introduction to factor models is brief and from intuitive perspective.

This study introduces a deep learning-based framework for portfolio optimization tailored to different investor risk preferences. We combine two prediction models, Long Short-Term Memory (LSTM) and One-Dimensional Convolutional Neural Network (1D-CNN), with three portfolio frameworks: Mean-Variance with Forecasting (MVF), Risk Parity Portfolio (RPP), and Maximum Drawdown Portfolio (MDP). Each framework represents a distinct risk profile: return-seeking, moderate-risk and conservative. The dataset is constructed from daily returns of VN-100 stocks in Vietnam, covering the period from 2017 to 2024. Forecasts from the deep learning models are integrated into each optimization approach. Results from the 2023–2024 test period showed that LSTM outperforms 1D-CNN in both accuracy and stability. Portfolios using LSTM achieved better performance. LSTM+MVF delivers the best risk-adjusted returns, while LSTM+MDP achieves the highest total return. The study highlights the value of aligning predictive models with appropriate optimization strategies for improved investment outcomes. Future work may include other asset classes, transaction cost modeling, and dynamic rebalancing. Combining deep learning with macroeconomic or alternative data could also improve forecasting and portfolio outcomes.

A toolkit for exploiting contemporaneous stock correlations

This thesis consists of three essays in financial economics, more precisely in the field of asset pricing and optimal portfolio choice. The first essay studies the asset pricing implications of heterogeneity in preferences coupled with long run risk in a dynamic general equilibrium pure exchange economy. It is concerned with the understanding of how risk sharing among different types of agents affects the assets returns moments in equilibrium and moreover, how these agents reacts dynamically to the long run risk that they perceive. The second and third essay approach the question of optimal portfolio with stochastic opportunity sets. The second essay proposes an approximation methodology for deriving approximate closed form solutions of dynamic optimal portfolio allocation problems. The third essay examines the optimal consumption, portfolio rule and life insurance purchase problem of a wage earner having uncertain lifetime when the market price of risk follows a mean-reverting stochastic process.

Predicting stock returns by classifier ensembles

Using an intangible intensity factor that is orthogonal to the Fama–French factors, we compare the role of intangible investment in predicting stock returns over the periods 1963–1992 and 1993–2022. For 1963–1992, intangible investment is weak in predicting stock returns, but for 1993–2022, the predictive power of intangible investment becomes very strong. Intangible investment has a significant impact not only on the MTB ratio (Fama–French high minus low [HML] factor) but also on operating profitability (OP) (Fama–French robust minus weak [RMW] factor) when forecasting stock returns from 1993 to 2022. For intangible asset‐intensive firms, intangible investment is the main predictor of stock returns, rather than MTB ratio and profitability. Our evidence suggests that intangible investment has become an important factor in explaining stock returns over time, independent of other factors such as profitability and MTB ratio.

Stock Return Predictability with Financial Ratios: Evidence from PSX 100 Index Companies

Developing a smart stock trading system equipped with a novel risk control mechanism for investors with different risk appetites