Applying Independent Component Analysis and Predictive Systems for Algorithmic Trading

Ceffer, Attila; Levendovszky, János; Fogarasi, Norbert

doi:10.1007/s10614-017-9719-z

Cited by 11 publications

(2 citation statements)

References 23 publications

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“…Moreover, Moneta and Pallante (2020) compare the performance of different ICA estimators within the field of structural vector autoregressive (SVAR) method on the US government spending and tax cuts data. Ceffer et al (2019) predict and model the future value of financial time series applying SVAR with ICA for pre-processing data. Liu et al (2019), on the other hand, apply performance-relevant kernel ICA for non-linear and non-Gaussian processes on the comprehensive economic index data.…”

Section: Literature Reviewmentioning

confidence: 99%

What Are the Macroeconomic Drivers of the Asset Returns of Turkish Banks?

Civan¹,

Şimşek

Çinar

2022

Technological and Economic Development of Economy

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The aim of this paper is to investigate the effects of the macroeconomic factors to the movements of the asset returns of the banks in Turkey in terms of systemic risk from 2005 to 2018. In the study, Independent Component Analysis is applied for extracting driving factors of the asset returns of Turkish banks by decomposing the returns into its components. After examining the relationship between the independent components and the macroeconomic variables, the results conclude that one component shows strong similarities with the well-known stock market index of Turkey, namely the BIST100. Besides, the BIST100 is observed as the most important macroeconomic indicator affecting the movements of the asset returns. From systemic risk perspective, the BIST100 and the exchange rate from US dollar to Turkish lira are interpreted as two macro factors that contribute to the systemic risk of Turkish banks. When it is reviewed the regression results of the estimated independent components with the macroeconomic variables, it is found that while the BIST100 affects the asset returns of Turkish banks on its own, three macroeconomic factors (the credit default swap spreads of Turkey, the exchange rate and volatility) jointly affect the banks by creating a chain effect.

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Section: Literature Reviewmentioning

confidence: 99%

What Are the Macroeconomic Drivers of the Asset Returns of Turkish Banks?

Civan¹,

Şimşek

Çinar

2022

Technological and Economic Development of Economy

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“…If the prediction is accurate enough, higher profits are expected with PMM. There is ample evidence in the literature that HFT performance can be improved significantly by accurate prediction of future prices, see, for example, [4][5][6]. However, as far as we know, there is no existing study on PMM in the literature.…”

Section: Introductionmentioning

confidence: 99%

Predictive Market Making via Machine Learning

et al. 2022

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Market making (MM) is an important means of providing liquidity to the stock markets. Recent research suggests that reinforcement learning (RL) can improve MM significantly in terms of returns. In the latest work on RL-based MM, the reward is a function of equity returns, calculated based on its current price, and the inventory of MM agent. As a result, the agent’s return is maximised and liquidity is provided. If the price movement is known and this information is optimally utilised, there is potential that the MM agent’s return can be further improved. Important questions are, how to predict stock price movement, and how to utilise such prediction? In this paper, we introduce the concept of predictive market marking (PMM) and present our method for PMM, which comprises a RL-based MM agent and a deep neural network (DNN)-based price predictor. A key component of PMM is the consolidated price equation (CPE), which amalgamates an equity’s present and predicted market prices into a consolidated price, which is used to generate ask and bid quotes that reflect both current price and future movement. Our PMM method is evaluated against the state-of-the-art (RL-based MM) and a traditional MM method, using ten stocks and three exchange traded funds (ETFs). Out-of-sample backtesting showed that our PMM method outperformed the two benchmark methods.

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A hybrid multicategory framework for generating automated trading systems

Kumar

Sahoo

Thakur

2023

Concurrency and Computation

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This work proposes a framework for producing automatic trading systems that suggest making trading decisions in financial markets. The trading systems thus generated utilize multicategory classifiers. A collection of important technical indicators are considered as input features for the underlying multicategory classifiers. The proposed trading systems also have an option to use random forest (RF) algorithms for feature selection. The trading range breakout strategy is used to train the proposed trading systems thus generating “BUY/SELL/WAIT” trading signals on daily open prices. The performances of the proposed trading systems are evaluated over five future indices. Empirical findings suggest that the day trading systems based on the proposed multicategory SVM classifiers along with the RF technique outperform the day trading systems built using the multicategory classifiers taken from the literature. The trading range breakout strategy‐based systems are found to be superior to the traditional BUY‐HOLD strategy and that of RF‐PSVM (BUY/SELL) based strategy.

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Applying Independent Component Analysis and Predictive Systems for Algorithmic Trading

Cited by 11 publications

References 23 publications

What Are the Macroeconomic Drivers of the Asset Returns of Turkish Banks?

What Are the Macroeconomic Drivers of the Asset Returns of Turkish Banks?

Predictive Market Making via Machine Learning

A hybrid multicategory framework for generating automated trading systems

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