Forecasting Stock Market Returns Over Multiple Time Horizons

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

doi:10.2139/ssrn.2646909

Cited by 4 publications

(11 citation statements)

References 47 publications

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“…Second, the model equations are in analytic form, which facilitates the interpretation of combined effects. Third, and more importantly, this model is supported by empirical tests: it can replicate past prices within reasonable tolerance and predict returns with a precision sufficient for designing a successful trading strategy (Kroujiline et al, 2016).…”

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confidence: 94%

“…Page | 9 information, expectation and price may be the relevant macro variables for describing market dynamics, and thus with the correct choice of hypotheses about how interactions occur at the micro level a dynamic model for the evolution of these variables can be developed that may be able to explain the observed market behaviors. Gusev et al (2015) developed, and Kroujiline et al (2016) extended, such a dynamic model.…”

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confidence: 99%

“…That paper also replicated, using empirically measured ℎ, the actual price path and return distribution of the US stock market with relatively good precision. Kroujiline et al (2016) derived a more general version of equations (11-13) to encompass investor groups with different investment horizons, thereby improving the model's precision, and applied this extended version for return prediction.…”

Section: Page | 17mentioning

confidence: 99%

“…This section augments the empirical studies in Gusev et al (2015) and Kroujiline et al (2016): it briefly reports the updated results of the empirical replication of market price path.…”

Section: Empirical Verificationmentioning

confidence: 99%

“…It is outside the scope of this paper to test the model's predictive power. We only note that Kroujiline et al (2016) developed, using the generalized version of equations (11-13), a return forecast methodology based on the analysis of the market position in the model's phase space and constructed an investment strategy upon it. This strategy, which has news as its only input, has been live traded since April 1, 2016.…”

Section: Page | 20mentioning

confidence: 99%

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An endogenous mechanism of business cycles

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This paper suggests that business cycles may be a manifestation of coupled real economy and stock market dynamics and describes a mechanism that can generate economic fluctuations consistent with observed business cycles. To this end, we seek to incorporate into the macroeconomic framework a dynamic stock market model based on opinion interactions (Gusev et al., 2015). We derive this model from microfoundations, provide its empirical verification, demonstrate that it contains the efficient market as a particular regime and establish a link through which macroeconomic models can be attached for the study of real economy and stock market interaction. To examine key effects, we link it with a simple macroeconomic model (Blanchard, 1981). The coupled system generates nontrivial endogenous dynamics, which exhibit deterministic and stochastic features, producing quasiperiodic fluctuations (business cycles). We also inspect this system's behavior in the phase space. The real economy and the stock market coevolve dynamically along the path governed by a stochastically-forced dynamical system with two stable equilibria, one where the economy expands and the other where it contracts, resulting in business cycles identified as the coherence resonance phenomenon. Thus, the incorporation of stock market dynamics into the macroeconomic framework, as presented here, allows the derivation of realistic behaviors in a tractable setting.Note that the price drift is positive provided > * 2 ⁄ , which means that, in the long run, good news reflective of technological progress must (approximately) exceed expectations to sustain the longterm growth.Hence, as expected, model (11-13) does not refute the efficient market model but instead encompasses it as a particular case. To sum up, the results reported so far enable us to reasonably conclude that model (11)(12)(13), derived from first principles, establishes a general, tractable and credible 14 Blanchard (1981) also included a fiscal policy term. We do not consider policy effects in this work. 15 Equation (17) can be obtained, as a limiting case, from a dynamic extension of Cobb-Douglas production,where output adjusts over time to changes in capital subject to credit frictions (Gusev et al, 2019).

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Section: Page |mentioning

confidence: 94%

Section: Page |mentioning

confidence: 99%

Section: Page | 17mentioning

confidence: 99%

“…This section augments the empirical studies in Gusev et al (2015) and Kroujiline et al (2016): it briefly reports the updated results of the empirical replication of market price path.…”

Section: Empirical Verificationmentioning

confidence: 99%

Section: Page | 20mentioning

confidence: 99%

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An endogenous mechanism of business cycles

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News media analytics in finance: a survey

Marty

Vanstone

Hahn³

2019

Accounting & Finance

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We survey the works applying text analytics to the study of news media in financial markets beyond intraday horizons, and expand into the fundamental economic theory and concepts relevant to the field. We compare and contrast the news sources, textual analysis methods and empirical modelling approaches adopted within the literature. We distil and categorise the key empirical insights, and summarise the bibliographic history of the literature so far. While this rapidly growing field has yielded many exciting discoveries, there are a number of promising avenues for future research which will only benefit from continued advances in computational technology.

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The Market Nanostructure Origin of Asset Price Time Reversal Asymmetry

Cordi

Challet

Kassibrakis³

2018

SSRN Journal

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We introduce a framework to infer lead-lag networks between the states of elements of complex systems, determined at different timescales. As such networks encode the causal structure of a system, infering lead-lag networks for many pairs of timescales provides a global picture of the mutual influence between timescales. We apply our method to two trader-resolved FX data sets and document strong and complex asymmetric influence of timescales on the structure of lead-lag networks. Expectedly, this asymmetry extends to trader activity: for institutional clients in our dataset, past activity on timescales longer than 3 hours is more correlated with future activity at shorter timescales than the opposite (Zumbach effect), while a reverse Zumbach effect is found for past timescales shorter than 3 hours; retail clients have a totally different, and much more intricate, structure of asymmetric timescale influence. The causality structures are clearly caused by markedly different behaviors of the two types of traders. Hence, market nanostructure, i.e., market dynamics at the individual trader level, provides an unprecedented insight into the causality structure of financial markets, which is much more complex than previously thought.PACS numbers PACS numbers.

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Forecasting Stock Market Returns Over Multiple Time Horizons

Cited by 4 publications

References 47 publications

An endogenous mechanism of business cycles

An endogenous mechanism of business cycles

News media analytics in finance: a survey

The Market Nanostructure Origin of Asset Price Time Reversal Asymmetry

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