<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The paper presents a new approach to optimizing automatic transactional systems. We propose a multi-stage technique which enables us to find investment strategies <italic>beating the market</italic>. Additionally, new measures of combined risk and returns are applied in the process of optimization. Moreover, we define new elements of a risk control system based on volatility measures and consecutive signal confirmation. As a result, we formulate three complex investment systems which maximize returns and simultaneously minimize risk in comparison to all other alternative investments (<italic>IR=2, Maximum Drawdown&lt;21%, Maximum Loss Duration=0.75 year</italic>). Our analysis is based on historical daily data (1998-2010, in- and out-of-sample period) for index and commodity futures. Afterwards, the systems are reoptimized and reallocated each half a year in order to include the most recent financial data. Finally, we show the results for a joint model consisting of our three systems.</p></abstract>

The paper presents a new approach to optimizing automatic transactional systems. We propose a multi-stage technique which enables us to find investment strategies beating the market. Additionally, new measures of combined risk and returns are applied in the process of optimization. Moreover, we define new elements of a risk control system based on volatility measures and consecutive signal confirmation. As a result, we formulate three complex investment systems which maximize returns and simultaneously minimize risk in comparison to all other alternative investments (IR=2, Maximum Drawdown<21%, Maximum Loss Duration=0.75 year). Our analysis is based on historical daily data (1998-2010, in- and out-of-sample period) for index and commodity futures. Afterwards, the systems are reoptimized and reallocated each half a year in order to include the most recent financial data. Finally, we show the results for a joint model consisting of our three systems.

Investment Strategies that Beat the Market. What Can We Squeeze from the Market?

Department Director of Credit Risk Management for Micro-entrepreneurs

Financial Internet Quarterly

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Investment Strategies that Beat the Market. What Can We Squeeze from the Market?"}

The paper presents a new approach to optimizing automatic transactional systems. We propose a multi-stage technique which enables us to find investment...

Investment Strategies that Beat the Market. What Can We Squeeze from the Market?

Data publikacji: 15 kwi 2019

Zakres stron: 36 - 55

Otrzymano: 20 lut 2017

Przyjęty: 20 gru 2018

DOI: https://doi.org/10.2478/fiqf-2018-0026

Słowa kluczowe
investment strategies, automatic trading systems, optimization, technical and fundamental analysis, market volatility, efficient risk and return measures, EMH, mutual and hedge funds,

© 2018 Robert Ślepaczuk et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Investment Strategies that Beat the Market. What Can We Squeeze from the Market?

Data publikacji: 15 kwi 2019

Zakres stron: 36 - 55

Otrzymano: 20 lut 2017

Przyjęty: 20 gru 2018

DOI: https://doi.org/10.2478/fiqf-2018-0026

Słowa kluczoweinvestment strategies, automatic trading systems, optimization, technical and fundamental analysis, market volatility, efficient risk and return measures, EMH, mutual and hedge funds,

© 2018 Robert Ślepaczuk et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
investment strategies, automatic trading systems, optimization, technical and fundamental analysis, market volatility, efficient risk and return measures, EMH, mutual and hedge funds,