Analysis of Selected Seasonality Effects in the Following Metal Markets: Gold, Silver, Platinum, Palladium and Copper

Borowski, Krzysztof; Lukasik, Malgorzata

doi:10.2139/ssrn.2598514

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…He found that there is no low or negative return on that day, contrarily to what we can believe. Borowski and Lukasik () examined the day‐of‐the‐week effect and the weekend effect in precious metals’ prices from 1994 to 2014 and found that there is no monthly effect but the weekend effect for gold prices. On the other hand, Apergis and Eleftheriou () found that business cycles asymmetrically affect gold returns that respond stronger during the recessionary phases than during booming phases.…”

Section: Literature Review On the Price Seasonality Of Financial Assetsmentioning

confidence: 99%

The seasonality of gold prices in China does the risk‐aversion level matter?

Hoang

Zhu

Xiao

et al. 2018

Accounting & Finance

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This article aims to investigate the seasonality of gold prices at the Shanghai Gold Exchange over the 2002-2016 period. Our contributions rely in the distinction between risk-averse and risk-seeking investors regarding their investment strategies. The results show the existence of positive Monday and January effects. However, the Monday effect is more suitable to risk-seeking investors while the January effect is more suitable to risk-averse investors in bearish periods only. A robustness check shows that the Monday effect does not hold on gold futures prices. These results indicate the importance to consider the risk-aversion level of investors in seasonal investment strategies.

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Section: Literature Review On the Price Seasonality Of Financial Assetsmentioning

confidence: 99%

The seasonality of gold prices in China does the risk‐aversion level matter?

Hoang

Zhu

Xiao

et al. 2018

Accounting & Finance

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show abstract

“…To include the 'day of the week effect', meaning more negative returns on Mondays, and more positive returns on Fridays, found in previous research in the copper markets [61][62][63], we created a dummy variable representing the day of the week; 1 for Mondays, 2 for Tuesdays, Wednesdays and Thursdays, and 3 for Fridays.…”

Section: Datamentioning

confidence: 99%

Artificial Intelligence Modelling Framework for Financial Automated Advising in the Copper Market

Suárez

García-Fernández

Gallardo

2019

Journal of Open Innovation: Technology, Market, and Complexity

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Financial innovation by means of Fintech firms is one of the more disruptive business model innovations from the latest years. Specifically, in the financial advisor sector, worldwide assets under management of artificial intelligence (AI)-based investment firms, or robo-advisors, currently amount to US$975.5 B. Since 2008, robo-advisors have evolved from passive advising to active data-driven investment management, requiring AI models capable of predicting financial asset prices on time to switch positions. In this research, an artificial neural network modelling framework is specifically designed to be used as an active data-driven robo-advisor due to its ability to forecast with today’s copper prices five days ahead of changes in prices using input data that can be fed automatically in the model. The model, tested using data of the two periods with a higher volatility of the returns of the recent history of copper prices (May 2006 to September 2008 and September 2008 to September 2010) showed that the method is capable of predicting in-sample and out-of-sample prices and consequently changes in prices with high levels of accuracy. Additionally, with a 24-day window of out-of-sample data, a trading simulation exercise was performed, consisting of staying long if the model predicts a rise in price or switching to a short position if the model predicts a decrease in price, and comparing the results with the passive strategies, buy and hold or sell and hold. The results obtained seem promising in terms of both statistical and trading metrics. Our contribution is twofold: 1) we propose a set of input variables based on financial theory that can be collected and fed automatically by the algorithm. 2) We generate predictions five days in advance that can be used to reposition the portfolio in active investment strategies.

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Analysis of Selected Seasonality Effects in the Following Metal Markets: Gold, Silver, Platinum, Palladium and Copper

Cited by 2 publications

References 33 publications

The seasonality of gold prices in China does the risk‐aversion level matter?

The seasonality of gold prices in China does the risk‐aversion level matter?

Artificial Intelligence Modelling Framework for Financial Automated Advising in the Copper Market

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