Price Index Modeling and Risk Prediction of Sharia Stocks in Indonesia

This study aimed to predict the JKII (Jakarta Islamic Index) price as a price index of sharia stocks and predict the loss risk. This study uses geometric Brownian motion (GBM) and Value at Risk (VaR; with the Monte Carlo Simulation approach) on the daily closing price of JKII from 1 August 2020–13 August 2021 to predict the price and loss risk of JKII at 16 August 2021–23 August 2021. The findings of this study were very accurate for predicting the JKII price with a MAPE value of 2.03%. Then, using VaR with a Monte Carlo Simulation approach, the loss risk prediction for 16 August 2021 (one-day trading period after 13 August 2021) at the 90%, 95%, and 99% confidence levels was 2.40%, 3.07%, and 4.27%, respectively. Most Indonesian Muslims have financial assets in the form of Islamic investments as they offer higher returns within a relatively short time. The movement of all Islamic stock prices traded on the Indonesian stock market can be seen through the Islamic stock price index, namely the JKII (Jakarta Islamic Index). Therefore, the focus of this study was predicting the price and loss risk of JKII as an index of Islamic stock prices in Indonesia. This study extends the previous literature to determine the prediction of JKII price and the loss risk through GBM and VaR using a Monte Carlo simulation approach.

Hybrid quantum investment optimization with minimal holding period

In this paper we propose a hybrid quantum-classical algorithm for dynamic portfolio optimization with minimal holding period. Our algorithm is based on sampling the near-optimal portfolios at each trading step using a quantum processor, and efficiently post-selecting to meet the minimal holding constraint. We found the optimal investment trajectory in a dataset of 50 assets spanning a 1 year trading period using the D-Wave 2000Q processor. Our method is remarkably efficient, and produces results much closer to the efficient frontier than typical portfolios. Moreover, we also show how our approach can easily produce trajectories adapted to different risk profiles, as typically offered in financial products. Our results are a clear example of how the combination of quantum and classical techniques can offer novel valuable tools to deal with real-life problems, beyond simple toy models, in current NISQ quantum processors.

Evaluation of dynamic cointegration-based pairs trading strategy in the cryptocurrency market

Purpose This paper aims to demonstrate a dynamic cointegration-based pairs trading strategy, including an optimal look-back window framework in the cryptocurrency market and evaluate its return and risk by applying three different scenarios. Design/methodology/approach This study uses the Engle-Granger methodology, the Kapetanios-Snell-Shin test and the Johansen test as cointegration tests in different scenarios. This study calibrates the mean-reversion speed of the Ornstein-Uhlenbeck process to obtain the half-life used for the asset selection phase and look-back window estimation. Findings By considering the main limitations in the market microstructure, the strategy of this paper exceeds the naive buy-and-hold approach in the Bitmex exchange. Another significant finding is that this study implements a numerous collection of cryptocurrency coins to formulate the model’s spread, which improves the risk-adjusted profitability of the pairs trading strategy. Besides, the strategy’s maximum drawdown level is reasonably low, which makes it useful to be deployed. The results also indicate that a class of coins has better potential arbitrage opportunities than others. Originality/value This research has some noticeable advantages, making it stand out from similar studies in the cryptocurrency market. First is the accuracy of data in which minute-binned data create the signals in the formation period. Besides, to backtest the strategy during the trading period, this study simulates the trading signals using best bid/ask quotes and market trades. This study exclusively takes the order execution into account when the asset size is already available at its quoted price (with one or more period gaps after signal generation). This action makes the backtesting much more realistic.

Using Statistical Model to Study the Daily Closing Price Index in the Kingdom of Saudi Arabia (KSA)

Classification in statistics is usually used to solve the problems of identifying to which set of categories, such as subpopulations, new observation belongs, based on a training set of data containing information (or instances) whose category membership is known. The article aims to use the Gaussian Mixture Model to model the daily closing price index over the period of 1/1/2013 to 16/8/2020 in the Kingdom of Saudi Arabia. The daily closing price index over the period declined, which might be the effect of corona virus, and the mean of the study period is about 7866.965. The closing price is the last regular deal that took place during the continuous trading period. If there are no transactions on the stock during the day, the closing price is the previous day’s closing price. The closing auction period comes after the continuous trading period (from 3 : 00 PM to 3 : 10 PM), during which investors can enter by buying and selling the stocks at this period. The experimental results show that the best mixture model is E (equal variance) with three components according to the BIC criterion. The expectation-maximization (EM) algorithm converged in 2 repetitions. The data source is from Tadawul KSA.

Dynamic Adjustment Model of the Water Rights Trading Price Based on Water Resource Scarcity Value Analysis

The formation of the water rights trading price is an important part of the water rights trading system. In order to continuously and dynamically reflect the social–economic development changes and water resource scarcity, herein, we discuss the dynamic adjustment of the water rights trading price from the perspective of water resource scarcity value analysis. First, we constructed the water resource scarcity evaluation index system from the four dimensions of the water resource natural endowment, the water resource supply, the water resource demand, and the water environment, and then we constructed the water resource scarcity index calculation model of the transferor, the transferee, and the comprehensive water resource scarcity index calculation model of both parties of the trading. Second, by analyzing the 30 comparable water rights trading cases in China since 2016, we established the response function of the water rights trading price to the water resource scarcity index, and then we analyzed the impact mechanism of the water resource scarcity index on the water rights trading price. Third, based on the two factors of “the water resource scarcity value” and “the capital time value,” we constructed a dynamic price adjustment model of water rights trading for different adjustment factors, so as to adjust the water rights trading price scientifically. Finally, we took the water rights trading in Helan County (Helan) of Ningxia Hui Autonomous Region (Ningxia) as an example. The research shows that: (1) During the trading period of water rights in Helan, the water resource scarcity index rises, and the water rights trading price should be increased year-by-year. Additionally, there are certain differences in the water rights trading price changes with the adjustment of different elements. Among them, considering the adjustment of “the water resource scarcity value” element, the water rights trading price of Helan should be increased from 1.0940 to 2.8574 CNY/m³ during the water rights trading period; (2) there are differences in the water rights trading cost under different payment modes, among which the annual payment mode increased the most, i.e., from 2.7350 × 108 to 7.4500 × 108 CNY. This study suggests exerting a regulating effect of the water scarcity value on the water rights trading price, so as to promote the construction of a more equitable and long-term water rights trading market.

Using algorithmic trading to analyze short term profitability of Bitcoin

Cryptocurrencies such as Bitcoin (BTC) have seen a surge in value in the recent past and appeared as a useful investment opportunity for traders. However, their short term profitability using algorithmic trading strategies remains unanswered. In this work, we focus on the short term profitability of BTC against the euro and the yen for an eight-year period using seven trading algorithms over trading periods of length 15 and 30 days. We use the classical buy and hold (BH) as a benchmark strategy. Rather surprisingly, we found that on average, the yen is more profitable than BTC and the euro; however the answer also depends on the choice of algorithm. Reservation price algorithms result in 7.5% and 10% of average returns over 15 and 30 days respectively which is the highest for all the algorithms for the three assets. For BTC, all algorithms outperform the BH strategy. We also analyze the effect of transaction fee on the profitability of algorithms for BTC and observe that for trading period of length 15 no trading strategy is profitable for BTC. For trading period of length 30, only two strategies are profitable.

SIMULATION OF PARTICLE SWARM OPTIMIZATION FOR INVESTMENTS ON STOCK MARKET

This work reports simulations performed using Particle Swarm Optimization (PSO) as applied to investments on the stock market. About 480 stocks belonging to the S&P500 index have been taken into account. A naive approach has been developed in which one simulation step corresponded to one trading period. As a second ingredient of the investment strategy, the relative strength of an asset has been employed. The results are analyzed with respect to the parameters of PSO.

Towards the Design of P2P Energy Trading Scheme Based on Optimal Energy Scheduling for Prosumers

The peer-to-peer (P2P) energy trading is anchored in more efficient usage of electric power by allowing excess electric power from energy prosumers to be harnessed by other end-users. To boost the P2P energy trading, it is of pivotal significance to call on energy prosumers and end-users to actively participate in the trading while sharing information with a greater degree of freedom. In this perspective, this paper purports to implement the P2P energy trading scheme with an optimization model to assist in energy prosumers’ decisions by reckoning on hourly electric power available in the trading via the optimal energy scheduling of the energy trading and sharing system (ETS). On a purely practical level, it is assumed that all trading participants neither join the separate bidding processes nor are forced to comply with the predetermined optimal schedules for a trading period. Furthermore, this paper will be logically elaborated with reference to not only the determination of transaction price for maximizing the benefits of consumers under the different electricity rates but the establishment of additional settlement standards for bridging an imperative gap between optimally planned and actually transacted quantities of the P2P energy trading.

Pairs Trading in Commodity Futures: Evidence from the Indian Market

The pairs trading, one of the techniques of the statistical arbitrage, is a market-neutral trading strategy that employs time series methods to identify relative mispricing between securities based on the expected values of these assets. The main objective of this study was to investigate the profitability and risks of pairs trading based on the selection of pairs through minimising the sum of squared deviation (distance method) and the selection based on cointegration tests (cointegration method) using the future daily prices of commodities traded and listed on The Multi Commodity Exchange of India (MCX) over 2011-2017 on a rolling basis. The pairs trading strategy was performed in two stages: the formation period and the trading period. The strategy involved long position in one commodity and short position in other commodity of the pair identified. The study revealed that pairs trading in commodities were significantly profitable, with average annualised profitability of up to 59 percent, including transaction costs.