Tests of quantitative investing strategies of famous investors: case of Thailand

Sareewiwatthana, Paiboon; Janin, Patarapon

doi:10.21511/imfi.14(3-1).2017.06

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…Sareewiwatthana Paiboon and colleagues [12] conducted an in-depth investigation into the quantitative investment strategies employed by renowned investors in the Thailand Stock Exchange over the period spanning from 2002 to 2016. The outcomes of their research unveiled a particularly interesting finding: when stocks were ranked based on enterprise value (EBIT/EV), this approach yielded the highest risk-adjusted return among the 30 carefully selected stocks under scrutiny.…”

Section: Discussionmentioning

confidence: 99%

The Application and Prospects of Deep Learning in the Field of Quantitative Investment

Liu

2024

AEMPS

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This literature review provides a comprehensive overview of key developments in quantitative trading theory, machine learning-based financial time series forecasting, deep learning-based financial time series forecasting, and modern quantitative investment strategies. It highlights seminal contributions from renowned scholars and researchers in the field. This review first explores the Efficient Market Hypothesis (EMH) proposed by Eugene Fama in 1970 and its empirical validation, which lays the foundation for understanding stock market dynamics. It then focuses on the application of machine learning to financial time series forecasting, including Hulls Delta strategy, Hujll Js multifactor model regression series, and Junhua Chens seminal work in 2009, which emphasizes the role of machine learning in solving the challenges of financial time series data. Finally, an overview of the development of deep learning in financial time series forecasting is presented, including the comprehensive model of Bowie et al., the success of Junhua Chens Deep Belief Network (DBN), and the sequence data processing capabilities of the Multi-stage Attention Network (MAN) model. In terms of modern quantitative investment strategies, it covers research areas such as EBIT/EV-based stock ranking studies, higher-order moment analysis, frequent trading strategies, and investor sentiment indicators. In summary, this literature review showcases the evolution of quantitative trading theory, the emergence of machine learning and deep learning in financial time series forecasting, and the development of modern quantitative investment strategies, offering valuable insights and tools for investors, researchers, and practitioners in financial markets.

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Section: Discussionmentioning

confidence: 99%

The Application and Prospects of Deep Learning in the Field of Quantitative Investment

Liu

2024

AEMPS

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“…In general, the effectiveness of this model was empirically confirmed on the examples of Japan (Noma, 2010), the United Kingdom (Rathjens & Schellhove, 2011), the United States of America (Mohr, 2012;Krauss et al, 2015;Safdar, 2016;Turtle & Wang, 2017), several emerging markets (Hyde, 2013), India (Singh & Kaur, 2014;Tripathy & Pani, 2017), South Africa (Oyebode, 2016), Australia (Hyde, 2016), Germany (Pätäri, 2017), several European markets (Tikkanen & Äijö, 2018) (Walkshäusl, 2020). The comparisons of several investment strategies prepared, inter alia, by Almas and Duque (2008), Sareewiwatthana and Janin (2017) and Brindelid and Nilsson (2021) also show the effectiveness of the F-Score model (in some cases it was considered the most efficient from all analyzed models).…”

Section: Assessment Of F-score-type Models Efficiencymentioning

confidence: 99%

Building of F-Score-Like Models on the Example of the Polish Stock Market

Pilch

2023

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Theoretical background: Value investing is one of the most popular investing approaches. In their frame, there could be a high B/M investing strategy identified. F-Score, developed by Piotroski, is a scoring model applied to the sample of high B/M entities. Its purpose is to select companies with strong financial foundations and buy their shares for the investment portfolio to generate positive market-adjusted returns in the following periods. The effectiveness of this model was mostly empirically confirmed, especially regarding developed markets. Purpose of the article: The main aim of the paper was to build F-Score-like models based on the data from the Polish stock market. The main hypothesis concerned the higher effectiveness of such models than F-Score, as the specificity of a given market should result in a better fit to the data. Research methods: Building of the models based on the discriminant analysis and formation of the investment portfolios based on the indications of these models as well as F-Score. Finally, backtesting of the portfolios built to assess their effectiveness. The sample covered most of the Polish-listed companies. The period taken into account was 2012–2022. Main findings: Models built (X-Score and Y-Score) were less efficient than F-Score. Moreover, they led to generating negative rates of return (both raw and market-adjusted). On the other hand, using of F-Score for the analyzed period seems to be purposeful due to the 1.35% mean annual market-adjusted return generated. Apart from the scoring models analyzed, the research partially confirmed the advisability of using a high B/M investing strategy. Generally, the results obtained are in line with the findings of most of other authors –regarding the F-Score effectiveness. However, an approach based on Mohanram’s idea – using the differences between absolute values of a given variable and median from the sample – proved to be inadequate in the Polish stock market.

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“…Value Investing para criar estratégias de investimento baseada em indicadores fundamentalistas Sareewiwatthana (2017). Essas estratégias estão resumidas abaixo:…”

Section: Diversos Investidores Aclamados Internacionalmente Utilizara...unclassified

“…Segue na tabela 1 os retornos de cada uma dessas estratégias no mercado tailandês contra o índice de mercado SET TRI, construída por Sareewiwatthana (2017).…”

Section: Diversos Investidores Aclamados Internacionalmente Utilizara...unclassified

Análise do desempenho da Magic Formula aplicada ao mercado de capitais brasileiro

Conceição

2022

Revista PO: R. Eletr. de Eng. de Produção e Correlatas

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O presente estudo tem como objetivo analisar o desempenho da Magic Formula no mercado de capitais do Brasil, reunindo evidências sobre a existência do Value Premium e eficiência dessa metodologia no processo de formação de carteiras de investimento através da utilização de indicadores fundamentalistas no mercado de capitais brasileiro. Desenvolvida por Joel Greenblatt, a Magic Formula é uma metodologia de formação de carteiras que consiste em escolher ações com altos Returns on Capital e Earnings Yields, seguindo a filosofia de Value Investing. Diversas carteiras foram montadas no período de 2006 a 2020 utilizando diferentes combinações de número de ativos por carteira e períodos depermanência. Todas as carteiras apresentaram retornos superiores ao Ibovespa. As diferenças entre os retornos médios anualizados das carteiras e o do Ibovespa foram significativas, sendo que a carteira com pior desempenho apresentou CAGR de 15,0% contra 7,8% do Ibovespa. As carteiras também obtiveram resultados positivos após serem ajustadas pelo risco. A pior razão retorno-volatilidade foi de 0,12, comparado a -0,07 do Ibovespa. Todas as carteiras de pior pontuação apresentaram resultados inferiores ao Ibovespa, em linha com as expectativas e os resultados observados na grande maioria dos trabalhos.

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Tests of quantitative investing strategies of famous investors: case of Thailand

Cited by 5 publications

References 17 publications

The Application and Prospects of Deep Learning in the Field of Quantitative Investment

The Application and Prospects of Deep Learning in the Field of Quantitative Investment

Building of F-Score-Like Models on the Example of the Polish Stock Market

Análise do desempenho da Magic Formula aplicada ao mercado de capitais brasileiro

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