An Enriched Time-Series Forecasting Framework for Long-Short Portfolio Strategy

Abstract

Stock return forecasting typically requires a large number of factors and these factors usually exhibit nonlinear relations with each other. Conventional methods of stock return forecasting mainly fall into two categories: Technical Analysis and Fundamental Analysis. Technical Analysis focuses on time-series data, while Fundamental Analysis explores low-frequency fundamental variables. Although there are substantial works on either time-series analysis or fundamental analysis, few studies have enriched the time-series forecasting with fundamental variables, as the features are characterized by different frequencies, scales and types. In this paper, we propose a Long Short-Term Memory and Deep Neural Network (LSTM-DNN) hybrid model to integrate the fundamental information into time-series forecasting tasks. We demonstrate how investors can benefit from the superior performance of LSTM-DNN by constructing a long-short portfolio that takes long positions in stocks with the highest forecasting returns and short positions in stocks that are expected to decline. Extensive experimental results on real data show that our novel framework could improve the profitability of long-short portfolio strategies compared to the state-of-the-art approaches. We also find evidence indicating that the outperformance of LSTM-DNN model comes from its enhanced ability to extract information from the nonlinear relations among various features, rather than bearing more market risks. Besides the novel framework, we propose a cross-section normalization method, which benefits the framework by providing enriched cross-section signals. INDEX TERMS Hybrid deep neural networks, long-short portfolio strategy, security returns forecasting.

Enhancing Analytical Precision in Company Earnings Reports through Neurofuzzy System Development: A Comprehensive Investigation

“…Traditionally, price analysis is divided into fundamental and technical analysis [11]. Te stock price can be afected by news, events, and information about the release of new products.…”

“…Since we previously said that we avoid the strategy of holding stocks for a long time, our algorithm will be based on making a proft during one trading day. Although studies [11,15,19] indicate that good profts can be expected during the month, the indicators of a good fnancial report set the general trend in the dynamics of the company's stock price chart. However, we still want to maximize the annual income of the portfolio by increasing trading operations with a small amount of invested money.…”

Learning to Rank for Multi-Step Ahead Time-Series Forecasting

“…The existence of certain financial derivative instruments enable the monetization of the forecasted TTE map by our proposed models. Typical examples include future contracts, which enable the construction of a long-short investment portfolio [25], [84], [85]. With proper position sizing [86] and risk management, the signals or forecast derived from the model can be transformed into an investment strategy.…”

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

“…The third type of work (Classification), classifies indicator data from a stock as "Buy", "Hold", or "Sell" through deep learning and neural network based classifications [31], [32], [33], [34], [16]. Prediction results can also represent an "Up" or "Down" trend so that investors can make decisions on investment entry positions by applying two single non-linear classifiers ANN, SVM and one RF ensemble approach to predict the direction of the next day's movement [35], [5], [36].…”

“…For example, [10] used indicator technical data to predict stocks on the China Shanghai Stock Exchange market, then [11] proposed stock predictions using a combination of types of technical and macroeconomic indicator data sets. combining technical indicators and news sentiment through text mining techniques such as [12], [13], [14], [4], [15], research [16], [3], [17] also found that the company's fundamentals had a positive impact on changes in the company's stock price.…”