Convert index trading to option strategies via LSTM architecture

AbstractIn the past, most strategies were mainly designed to focus on stocks or futures as the trading target. However, due to the enormous number of companies in the market, it is not easy to select a set of stocks or futures for investment. By investigating each company's financial situation and the trend of the overall financial market, people can invest precisely in the market and choose to go long or short. Moreover, how to determine the position size of the transaction is also a problematic issue. In the past, many money management theories were based on the Kelly criterion. And they put a certain percentage of their total funds into the market for trading. Nonetheless, three massive problems cannot be overcome. First, futures are leveraged transactions, and extra funds must be deposited as margin. It causes that the position size is hard to be estimated by the Kelly criterion. The second point is that the trading strategy is difficult to determine the winning rate in the financial market and cannot be brought into the Kelly criterion to calculate the optimal fraction. Last, the financial data are always massive. A big data technique should be applied to resolve this issue and enhance the performance of the framework to reveal knowledge in the financial data. Therefore, in this paper, a concept of converting the original futures trading strategy into options trading is proposed. An LSTM (long short-term memory)-based framework is proposed to predict the profit probability of the original futures strategy and convert the corresponding daily take-profit and stop-loss points according to the delta value of the options. Finally, the proposed framework brings the results into the Kelly criterion to get the optimal fraction of options trading. The final research results show that options trading is closer to the optimal fraction calculated by the Kelly criterion than futures trading. If the original futures trading strategy can profit, the benefits after converting to options trading can be further superior

On the profitability of technical trading

The sole use of price and related summary statistics in a technical trading strategy is an anathema to weak-form market efficiency. In practice, however, traders actively use technical analysis to make investment decisions which makes this an important, but often neglected, area for study. This thesis includes four empirical chapters, which provide important evidence on the profitability of technical trading. The results from the detailed analysis undertaken in this thesis have broad relevance to both academics and those in the investment community. Existing research has been predominantly confined to evaluating basic technical trading rules, such as moving averages. Crucially, this ignores chart patterns. Widely employed by practitioners, such patterns form a vital part of technical analysis. As the most important price pattern, the head and shoulders pattern is subjected to detailed and thorough examination in this thesis. A significant contribution is made by evaluating formations recognised and used by traders, in sharp contrast to limited existing studies. Furthermore, a new method is developed to establish how quickly profits from a head and shoulders strategy decay, which has important implications for traders. Existing research has identified both reversal and relative strength effects in financial asset returns. A key separator between these two findings is the formation and holding time over which portfolios of winners and losers are evaluated. Motivated by this, a very large sample of ultra high-frequency data is used to investigate intraday momentum and reversal effects. As well as being an important contribution to research in this field, the results are, once again, of relevance to practitioners. The need for further research into technical analysis is clearly demonstrated by point and figure charting. Whilst traders have made consistent use of the technique for around a century, the amount of existing research is extremely small. Point and figure has attractive data filtering properties, clear trading rules and is particularly suited to intraday technical analysis. Again, using a very large sample of high-frequency data, a detailed evaluation of the profitability of a point and figure trading strategy is undertaken

A Gap Analysis for the Implementation of the Global Climate Observing System Programme in Africa

This report is the main output from a Gap Analysis, financed by the United Kingdom's Department for International Development (DfID) as a major contribution to a Global Climate Observing System (GCOS) workshop to address climate and development issues in sub-Saharan Africa. The requirement is to assess key gaps in the use of climate information from the perspective of the decision-making community at household, community, district, national and regional levels, for a range of sectors, with particular emphasis on health, agriculture and water. The report was prepared by a group of people with broad, deep and diverse experience with climate sensitive development problems, and with climate and development communities in Africa. It was strengthened by review by a larger community of development experts

Applications of artificial neural networks in financial market forecasting

This thesis evaluates the utility of Artificial Neural Networks (ANNs) applied to financial market and macroeconomic forecasting. In application, ANNs are evaluated in comparison to traditional forecasting models to evaluate if their nonlinear and adaptive properties yield superior forecasting performance in terms of robustness and accuracy. Furthermore, as ANNs are data-driven models, an emphasis is placed on the data collection stage by compiling extensive candidate input variable pools, a task frequently underperformed by prior research. In evaluating their performance, ANNs are applied to the domains of: exchange rate forecasting, volatility forecasting, and macroeconomic forecasting. Regarding exchange rate forecasting, ANNs are applied to forecast the daily logarithmic returns of the EUR/USD over a short-term forecast horizon of one period. Initially, the analytic method of Technical Analysis (TA) and its sub-section of technical indicators are utilized to compile an extensive candidate input variable pool featuring standard and advanced technical indicators measuring all technical aspects of the EUR/USD time series. The candidate input variable pool is then subjected to a two-stage Input Variable Selection (IVS) process, producing an informative subset of technical indicators to serve as inputs to the ANNs. A collection of ANNs is then trained and tested on the EUR/USD time series data with their performance evaluated over a 5-year sample period (2012 to 2016), reserving the last two years for out of sample testing. A Moving Average Convergence Divergence (MACD) model serves as a benchmark with the in-sample and out-of-sample empirical results demonstrating the MACD is a superior forecasting model across most forecast evaluation metrics. For volatility forecasting, ANNs are applied to forecast the volatility of the Nikkei 225 Index over a short-term forecast horizon of one period. Initially, an extensive candidate input variable pool is compiled consisting of implied volatility models and historical volatility models. The candidate input variable pool is then subjected to a two-stage IVS process. A collection of ANNs is then trained and tested on the Nikkei 225 Index time series data with their performance evaluated over a 4-year sample period (2014 to 2017), reserving the last year for out-of-sample testing. A GARCH (1,1) model serves as a benchmark with the out-of-sample empirical results finding the GARCH (1,1) model to be the superior volatility forecasting model. The research concludes with ANNs applied to macroeconomic forecasting, where ANNs are applied to forecast the monthly per cent-change in U.S. civilian unemployment and the quarterly per cent-change in U.S. Gross Domestic Product (GDP). For both studies, an extensive candidate input variable pool is compiled using relevant macroeconomic indicator data sourced from the Federal Bank of St Louis. The candidate input variable pools are then subjected to a two-stage IVS process. A collection of ANNs is trained and tested on the U.S. unemployment time series data (UNEMPLOY) and U.S. GDP time series data. The sample periods are (1972 to 2017) and (1960 to 2016) respectively, reserving the last 20% of data for out of sample testing. In both studies, the performance of the ANNs is benchmarked against a Support Vector Regression (SVR) model and a Naïve forecast. In both studies, the ANNs outperform the SVR benchmark model. The empirical results demonstrate that ANNs are superior forecasting models in the domain of macroeconomic forecasting, with the Modular Neural Network performing notably well. However, the empirical results question the utility of ANNs in the domains of exchange rate forecasting and volatility forecasting. A MACD model outperforms ANNs in exchange rate forecasting both in-sample and out-of-sample, and a GARCH (1,1) model outperforms ANNs in volatility forecasting

Time series momentum: theory and practice

Time series momentum (TSM) is a significant component of many investment strategies, both explicitly and implicitly. While academic studies have confirmed long run excess return, other aspects of the strategy have received less attention. This research focuses on performance variations across economic conditions and on the return drivers of TSM and associated investment funds. The performance record is extend back to 1925, confirming long run performance and providing a large sample to analyse its relationship with economic conditions, with a number of links demonstrated. TSM underperforms for periods of up to four years immediately after financial crises, with returns at less than half the level of normal periods. A breakdown in market structure is associated with this. Serial auto-correlation of asset returns, found in all markets at horizons of up to twelve months, is absent in the years following a crisis. Further evidence links the strategy to the business cycle, demonstrating underperformance in periods of recession and high economic uncertainty. A decomposition of returns of individual asset into systematic (macro-economic factor related) and idiosyncratic components shows that TSM generates profits from both components. The exposure of TSM to economic factors can explain part of the excess returns in an efficient market/arbitrage pricing theory framework. The performance of the commodity trading advisor (CTA) sector, closely associated with TSM, is analysed using a sample of 3,419 CTAs. A novel methodology eliminates biases and generates a reliable performance index back to 1987. This exhibits consistent excess returns over the period. Eight different CTA sub-strategies identified, exposed to a variety of risk premia in addition to TSM. Explanatory power is low with less than half of the returns associated with risk exposure. Three of the eight sub-strategies (representing half the funds) show exposure to TSM, each generating a statistically significant Sharpe ratio

Economic and Social Consequences of the COVID-19 Pandemic in Energy Sector

The purpose of the Special Issue was to collect the results of research and experience on the consequences of the COVID-19 pandemic for the energy sector and the energy market, broadly understood, that were visible after a year. In particular, the impact of COVID-19 on the energy sector in the EU, including Poland, and the US was examined. The topics concerned various issues, e.g., the situation of energy companies, including those listed on the stock exchange, mining companies, and those dealing with renewable energy. The topics related to the development of electromobility, managerial competences, energy expenditure of local government units, sustainable development of energy, and energy poverty during a pandemic were also discussed

Empirical essays on inferring information from options and other financial derivatives

This thesis consists of three essays on inferring information from option contracts and other financial derivatives in the U.S. market as well as in the international markets. The first essay examines corporate bankruptcy probabilities inferred from option prices and credit default swaps (CDS) spreads around the 2008 financial crisis in the U.S. market. Option pricing framework is used where the risk-neutral density of the underlying asset is assumed to be a mixture of two lognormals augmented with a probability of default, to calibrate to the market option prices. The CDS model assumes a constant default probability which is solved from the non-linear equation that equates the present value of expected premium payments with the present value of expected payoffs. The essay documents that both sources provide ex-ante bankruptcy probabilities, but there is no significant evidence suggesting one predicts the other. The second essay constructs volatility indices for 15 markets around the world and examines implied volatility spillover between these markets. Volatility indices are constructed using option prices based on the new VIX methodology with modification to address its limitations. Spillover effects are then examined using vector autoregressive analysis, impulse response functions and forecast error variance decomposition. Empirical results show that the U.S. is unambiguously the dominant source of uncertainty in the world. Correlation between markets largely depends on geographical proximity. The findings support the notion of informationally efficient international stock markets, in that information transmitted from one market to another is processed within one or two days. The third essay further investigates spillover effects in variance risk premiums, which has been interpreted as the difference between the realised variance under the physical measure and the risk-neutral measure. Realized variance under the physical measure is constructed for each market using the HAR-RV model, which is able to capture long-memory characteristic of volatility. Risk-neutral expectation of future variance is approximated by a portfolio of option contracts, as calculated in the second essay. Steps are taken to address serial correlation and dependence, and variance risk premium spillovers are examined using vector autoregressive analysis, impulse response functions, and Granger Causality tests. The findings are consistent with those found in implied volatility spillovers. The U.S. market is the distributor of uncertainty in the global market. Information transmitted from one market to another is quickly digested, but it may take longer in crisis period due to greater uncertainty

Time for a Nappy Change: beliefs and attitudes towards modern cloth nappies.

The United Nations Environment Programme highlights how the use of disposable nappies has become unsustainable, yet the practice of using modern cloth nappies (MCN) is niche. This study uses mixed methods of survey, story completion and focus group methods to explore how behaviour beliefs and attitudes to behaviour contribute to families’ decision making regarding the nappy system they use for their children. 1588 responded to the survey; 38 completed story completion activity; 24 participated in groups. This study finds that beliefs about the performance as a nappy, environmental credentials, financial considerations, laundry, effort, and hygiene differ according to the level of personal experience of using MCN. While beliefs about the environmentalcredentials of MCN create powerful drivers for the intention to use MCN, other beliefs about the upfront costs, laundry and effort contribute a negative attitude to MCN overall if their support network of other MCN users is not established. Current MCN users found using cloth nappy retailer websites, nappy libraries, and social media groups, including pre-loved and-sell groups, to be beneficial in improving attitude to MCN. This study concludes that interventions that simultaneously reduce or remove perceived barriers such as upfront costs, financial risks and too much effort, paired with campaigns which increase the likelihood of finding support, are more likely, than individual interventions, to be effective in increasing the number of families using MCN.Further study is needed to investigate the potential of interventions which reduce the financial risks such as, easy to access hire kits, spread the cost of MCN and pre-natal and newborn public services such as midwives and health visitors being well informed and encouraging of the use of MCN.<br/