There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

计量经济分析 = Econometric analysis

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Investor Psychology and Security Market Under- and Overreactions

Annual Energy Outlook 2008 With Projections to 2030

This study proposes refinements to some weaknesses in the Relative Strength Index (RSI) model and tests its predictability over pre and post crisis periods for the most active USD based cur...

The implementation of an adjusted relative strength index model in foreign currency and energy markets of emerging and developed economies

International business literature has paid special attention to the study of internationalisation and entry mode strategies of firms from the manufacturing sector. Very few studies have been conducted that examine the international expansion of firms from the service industry. Today, the increasing involvement of service industries in the development of business activities has attracted significant attention. The rapid growth of the international presence of service firms in the international arena has created a new area of interest for scholars to investigate the issue in greater detail. This paper reflects an initial investigation of the pattern of internationalisation, motivating factors, and the choice of entry strategy made by Malaysian service firms. As a successful developing country with a fast growing economy, the Malaysian government has facilitated and encouraged the presence of its firms in international markets. This research utilizes a case study approach through in-depth interviews with key managers/executives in the selected Malaysian service firms to explain the internationalization pattern and behaviour of these firms. Suggestions for further research are discussed.

https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=2345640

Explaining the Internationalization Process of Malaysian Service Firms

The aim of this study is to test a trading system based on the average directional index, which is complemented with the parabolic stop and reverse indicator. The trend-based system is tested onto the most actively traded USD based foreign currency pairs, using both monthly and weekly data set over 2000–2018. Sharpe and Sortino measures are used to track the performance of the currency pairs, based on total risk and downside risk assumptions. Results are robust tested by decomposing the data into pre and post 2008 financial crisis. Using an investment horizon over 18 years, the reliance upon the monthly model produced lower maximum drawdowns and lesser trades than the weekly model. While Swiss Franc had the best (worse) performance in the monthly (weekly) based model, the Chinese Renminbi witnessed the worse (best) performance in the monthly (weekly) based model. Pre and post financial crisis decompositions suggest the weekly-based system is more reliable than the monthly one with relatively more trades and positive performance, where the Chinese Renminbi and Japanese Yen posted the highest Sharpe and Sortino values of 0.996 and 4.452 respectively in the post crisis period. Proportionately high level of negative returns coupled with relatively low positive Sharpe and Sortino values, however, suggest that a trading system relying on the average directional index and parabolic stop and reverse indicator to be further tested and analyzed at higher frequencies.

/pdf/performance-of-the-average-directional-index-as-a-market-4405zgdfe3.pdf

Performance of the Average Directional Index as a market timing tool for the most actively traded USD based currency pairs

The purpose of this paper is to shed fresh light into whether an energy commodity price index (ENFX) and energy blockchain-based crypto price index (ENCX) can be used to predict movements in the energy commodity and energy crypto market.,Using principal component analysis over daily data of crude oil, heating oil, natural gas and energy based cryptos, the ENFX and ENCX indices are constructed, where ENFX (ENCX) represents 94% (88%) of variability in energy commodity (energy crypto) prices.,Natural gas price movements were better explained by ENCX, and shared positive (negative) correlations with cryptos (crude oil and heating oil). Using a vector autoregressive model (VAR), while the 1-day lagged ENCX (ENFX) was significant in estimating current ENCX (ENFX) values, only lagged ENCX was significant in estimating current ENFX. Granger causality tests confirmed the two markets do not granger cause each other. One standard deviation shock in ENFX had a negative effect on ENCX. Weak forecasting results of the VAR model, support the two markets are not robust forecasters of each other. Robustness wise, the VAR model ranked lower than an autoregressive model, but higher than a random walk model.,Significant structural breaks at distinct dates in the two markets reinforce that the two markets do not help to predict each other. The findings are limited by the existence of bubbles (December 2017-January 2018) which were witnessed in energy blockchain-based crypto markets and natural gas, but not in crude oil and heating oil.,As per the authors’ knowledge, this is the first paper to analyze the relationship between leading energy commodities and energy blockchain-based crypto markets.

Can energy commodities affect energy blockchain-based cryptos?

In this paper, we compare various approaches to stock price prediction using neural networks. We analyze the performance fully connected, convolutional, and recurrent architectures in predicting the next day value of S&P 500 index based on its previous values. We further expand our analysis by including three different optimization techniques: Stochastic Gradient Descent, Root Mean Square Propagation, and Adaptive Moment Estimation. The numerical experiments reveal that a single layer recurrent neural network with RMSprop optimizer produces optimal results with validation and test Mean Absolute Error of 0.0150 and 0.0148 respectively.

Ikhlaas Gurrib

Papers

The implementation of an adjusted relative strength index model in foreign currency and energy markets of emerging and developed economies

Explaining the Internationalization Process of Malaysian Service Firms

Performance of the Average Directional Index as a market timing tool for the most actively traded USD based currency pairs

Can energy commodities affect energy blockchain-based cryptos?

Stock price forecast with deep learning