Forecasting stock market returns over multiple time horizons

Kroujiline, Dimitri; Gusev, Maxim; Ushanov, Dmitry; Sharov, Sergey V.; Govorkov, Boris

doi:10.1080/14697688.2016.1176241

Cited by 12 publications

(3 citation statements)

References 34 publications

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“…According to the efficient market hypothesis (EMH), 1 it is impossible to forecast stock prices. However, some studies 2,3 have shown that stock prices and returns can be predicted to a certain degree. Early stock price prediction methods, such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), autoregressive conditional heteroskedasticity (ARCH), and generalized autoregressive conditional heteroskedasticity (GARCH) were based on statistical linear prediction models.…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach for portfolio construction: Combing two‐stage ensemble forecasting model with portfolio optimization

Chen,

Liu,

Jia

2023

Computational Intelligence

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Combining the stock prediction with portfolio optimization can improve the performance of the portfolio construction. In this article, we propose a novel portfolio construction approach by utilizing a two‐stage ensemble model to forecast stock prices and combining the forecasting results with the portfolio optimization. To be specific, there are two phases in the approach: stock prediction and portfolio optimization. The stock prediction has two stages. In the first stage, three neural networks, that is, multilayer perceptron (MLP), gated recurrent unit (GRU), and long short‐term memory (LSTM) are used to integrate the forecasting results of four individual models, that is, LSTM, GRU, deep multilayer perceptron (DMLP), and random forest (RF). In the second stage, the time‐varying weight ordinary least square model (OLS) is utilized to combine the first‐stage forecasting results to obtain the ultimate forecasting results, and then the stocks having a better potential return on investment are chosen. In the portfolio optimization, a diversified mean‐variance with forecasting model named DMVF is proposed, in which an average predictive error term is considered to obtain excess returns, and a 2‐norm cost function is introduced to diversify the portfolio. Using the historical data from the Shanghai stock exchange as the study sample, the results of the experiments indicate the DMVF model with two‐stage ensemble prediction outperforms benchmarks in terms of return and return‐risk characteristics.

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Section: Introductionmentioning

confidence: 99%

A hybrid approach for portfolio construction: Combing two‐stage ensemble forecasting model with portfolio optimization

Chen,

Liu,

Jia

2023

Computational Intelligence

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“…In other words, we emphasize strategic complementarity and peer influence that cause managers to coalign their individual expectations at the micro level. We use the framework developed in Gusev et al (2015) and Kroujiline et al (2016) to describe this interaction process mathematically and derive the macroscopic equations governing the dynamics of aggregate capital demand. To close the economy while highlighting the demand-driven effects, we attach these equations to a simple supply side component represented by the Solow growth model (1956).…”

Section: Introductionmentioning

confidence: 99%

“…This modeling approach adapts the investor-analyst interaction framework, developed for the stock market inGusev et al (2015) andKroujiline et al (2016), to the macroeconomic context.10 This treatment, known as the mean-field approach, is the leading-order approximation for a general interaction topology.…”

mentioning

confidence: 99%

Capital Demand Driven Business Cycles: Mechanism and Effects

Naumann-Woleske,

Benzaquen,

Gusev

et al. 2021

Preprint

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We develop a tractable macroeconomic model that captures dynamic behaviors across multiple timescales, including business cycles. The model is anchored in a dynamic capital demand framework reflecting an interactions-based process whereby firms determine capital needs and make investment decisions on a micro level. We derive equations for aggregate demand from this micro setting and embed them in the Solow growth economy. As a result, we obtain a closed-form dynamical system with which we study economic fluctuations and their impact on long-term growth. For realistic parameters, the model has two attracting equilibria: one at which the economy contracts and one at which it expands. This bi-stable configuration gives rise to quasiperiodic fluctuations, characterized by the economy's prolonged entrapment in either a contraction or expansion mode punctuated by rapid alternations between them. We identify the underlying endogenous mechanism as a coherence resonance phenomenon. In addition, the model admits a stochastic limit cycle likewise capable of generating quasiperiodic fluctuations; however, we show that these fluctuations cannot be realized as they induce unrealistic growth dynamics. We further find that while the fluctuations powered by coherence resonance can cause substantial excursions from the equilibrium growth path, such deviations vanish in the long run as supply and demand converge.

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