Forecasting the market return direction based on a time-varying probability density model

Gu, Wentao; Peng, Yiqing

doi:10.1016/j.techfore.2019.119726

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…This evidence explains why the theme of financial risk assessment is a classical issue in finance, and a special focus is clearly pointed to risk forecasting (see, e.g. the excellent monograph Alexander, 2008 but also the recent contributions in Borges, 2010;Castellano et al, 2020;Cooper et al, 2014;Cooper et al, 2021, Gu & Peng, 2019Kürüm et al, 2018, Meng & Taylor, 2020. We move from this premise and treat here the peculiar issue of explaining how data regularity can be helpful in predicting the market risk.…”

Section: Introductionmentioning

confidence: 91%

Statistical methods for decision support systems in finance: how Benford’s law predicts financial risk

2022

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This paper merges the statistical analysis of data regularities and decision support systems for investors. Specifically, it discusses the Benford’s law as a decision support device for financial investments. In particular, we illustrate the role of such a property of financial data as risk predictor for financial markets. First of all, we show empirical evidence of accordance between data on market index daily returns and Benford’s law. Then, we highlight that on short time period (1 year) the deviations from Benford’s law are related to low risk and positive trend periods; the p value of the $$\chi ^2$$ χ 2 test against the Benford’s distribution displays some predicting power for the market average return and risk level.

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Section: Introductionmentioning

confidence: 91%

Statistical methods for decision support systems in finance: how Benford’s law predicts financial risk

2022

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Alternative to Buy-and-Hold: Predicting Indices Direction and Improving Returns Using a Novel Hybrid LSTM Model

Beniwal¹,

Singh²,

Kumar³

2023

Int. J. Artif. Intell. Tools

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Predicting stock direction is challenging as stock price time series are extremely noisy. Moreover, the widely accepted efficient market hypothesis states that it is impossible to consistently generate excess returns than the market over a long-term horizon. Hence, the best approach for investors is thought to be the passive buy-and-hold strategy in indices. However, some researchers suggest that the market does have a predictable component. This paper’s objective is to provide investors with an alternative predictive system that generates an excess return over the classical buy-and-hold strategy and reduces risk. The authors propose an alternative investing model, Average True Range (ATR) and Momentum-based Long Short-Term Memory online (a-m-LSTM-o), that innovatively uses the technical indicators with Long Short-Term Memory (LSTM). Further, this study experiments with multiple other LSTM investing models using daily indices data of the world’s top five economies. Based on the prediction, the proposed model’s return is 172%, which is significantly higher than the buy-and-hold return of 139%, and it also has a lower drawdown of −49% compared to −51% for the buy-and-hold strategy. Hence, the authors suggest that the proposed model may be a good alternative to the passive approach of the investors.

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An interval constraint-based trading strategy with social sentiment for the stock market

Li,

Yang,

Lin

et al. 2024

Financ Innov

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Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics. At the same time, stock price forecasting that supports trading strategies is considered one of the most challenging tasks. Therefore, this study analyzes and extracts news media data, expert comments, social opinion data, and pandemic text data using natural language processing, and then combines the data with a deep learning model to forecast future stock price patterns based on historical stock prices. An interval constraint-based trading strategy is constructed. Using data from several typical stocks in the Chinese stock market during the COVID-19 period, the empirical studies and trading simulations show, first, that the sentiment composite index and the deep learning model can improve the accuracy of stock price forecasting. Second, the interval constraint-based trading strategy based on the proposed approach can effectively enhance returns and thus, can assist investors in decision-making.

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Forecasting the market return direction based on a time-varying probability density model

Cited by 3 publications

References 27 publications

Statistical methods for decision support systems in finance: how Benford’s law predicts financial risk

Statistical methods for decision support systems in finance: how Benford’s law predicts financial risk

Alternative to Buy-and-Hold: Predicting Indices Direction and Improving Returns Using a Novel Hybrid LSTM Model

An interval constraint-based trading strategy with social sentiment for the stock market

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