This thesis has applied the theory of real options to study forestry investment decision-making under stochastic timber prices. Suitable models have been developed for the stochastic timber prices, after addressing major issues in characterisation of the price process. First, the assumption of stochastic timber price process was based on detailed unit root tests, incorporating structural breaks in time-series analysis. The series was found to be stationary around shifting mean, justifying the assumption of mean reversion model. Due to shift in the mean, long-run mean to which the prices tended to revert could not be assumed constant. Accordingly, it was varied in discreet steps as per the breaks identified in the tests. The timber price series failed the normality test implying fat tails in the data. To account for these fat tails, ‘jumps’ were incorporated in the mean reversion model. The results showed that the option values for the jump model were higher than the mean reversion model and threshold levels for investment implied different optimal paths. Ignoring jumps could provide sub-optimal results leading to erroneous decisions. Second, the long-run mean to which prices reverted was assumed to shift continuously in a random manner. This was modeled through the incorporation of stochastic level and slope in the trend of the prices. Since the stochastic level and slope were not observable in reality, a Kalman-filter approach was used for the estimation of model parameters. The price forecasts from the model were used to estimate option values for the harvest investment decisions. Third, investment in a carbon sequestration project from managed forests was evaluated using real options, under timber price stochasticity. The option values and threshold levels for investment were estimated, under baseline and mitigation scenarios. Results indicated that carbon sequestration from managed forests might not be a viable investment alternative due to existing bottlenecks. Overall, the research stressed upon the need for market information and adaptive management, with a pro-active approach, for efficient investment decisions in forestry.

This study includes three issues about the dynamic of 30-days Taiwan Commercial Paper rate (CP2).The first issue focuses on the estimation of continuous-time short-term interest rate models. We discretize the continuous-time models by using two different approaches, and then use weekly and monthly data to estimate the parameters. The models are evaluated by data fit. We find that the estimated parameters are similar for different discretization approaches and would be more stable and efficient under quasi-maximum likelihood (QML) with weekly data. There exists mean reversion for Taiwan CP rate and the relationship between the volatility and the level of interest rates are less than 1 and smaller than that of American T-Bill rates reported by CKLS (1992) and Nowman (1997). We also find that CIR-SR model performs best for Taiwan CP rate. The second issue compares the continuous-time short-term interest rate models empirically both by predictive accuracy test and encompassing test. Having the estimated parameters of the models by discretization of Nowman(1997) and QML, we produce the forecasts on conditional mean and volatility for the interest rate over multiple-step-ahead horizons. The results indicate that the sophisticated models outperform the simpler models in the in-sample data fit, but have a distinct performance in the out-of-sample forecasting. The models equipped with mean reversion can produce better forecasts on conditional means during some period, and the heteroskedasticity variance model with outperform counterparts in volatility forecasting in some periods. The third issue concerns the persistent and massive volatility of short-term interest rates. This part inquires how the realizations on Taiwan short-term interest rates can be best described empirically. Various popular volatility specifications are estimated and tested. The empirical findings reveal that the mean reversion is an important characteristic for the Taiwan interest rates, and the level effect exists. Overall, the GARCH-L model fits well to Taiwan interest rates.

This study is to establish a binomial lattice method to apply real options theory to valuation and decision making in the petroleum exploration and production industry with a specific focus on the switching time from primary to water flooding oil recovery. First, West Texas Intermediate (WTI) historical oil price evolution in the past 25 years is studied and modeled with the geometric Brownian motion (GBM) and one-factor mean reversion price models to capture the oil price uncertainty. Second, to conduct real options evaluation, specific reservoir simulations are designed and oil production profile for primary and water flooding oil recovery for a synthetic onshore oil reservoir is generated using UTCHEM reservoir simulator. Third, a cash flow model from producing the oil reservoir is created with a concessionary fiscal system. Finally, the binomial lattice real options evaluation method is established to value the project with flexibility in the switching time from primary to water flooding oil recovery under uncertain oil prices. The research reaches seven conclusions: 1) for the GBM price model, the assumptions of constant drift rate and constant volatility do not hold for WTI historical oil price; 2) one-factor mean reversion price model is a better model to fit the historical WTI oil prices than the GBM model; 3) the evolution of historical WTI oil prices from January 2, 1986 to May 28, 2010 was according to three price regimes with different long run prices; 4) the established real options evaluation method can be used to identify the best time to switch from primary to water flooding oil recovery using stochastic oil prices; 5) with the mean reversion oil price model and the most updated cost data, the real options evaluation method finds that the water flooding switching time is earlier than the traditional net present value (NPV) optimizing method; 6) the real options evaluation results reveals that most of time water flooding should start when oil price is high, and should not start when oil price is low; and 7) water flooding switching time is sensitive to oil price model to be used and to the investment and operating costs. / text

Diss. Stockholm : Handelshögskolan, 2003.

This thesis investigates the long- and short- run stability of Cointegrated dual share equity pairs on the Swedish Equity Market. Testing for a cointegrated relationship on each pair are executed for a 13 year period to establish the cointegrated pairs. The stability of each cointegrated pair is then estimated using a rolling two year period. An Arbitrage Trading strategy is applied to the cointegrated pairs for the following one year period. The long-run relationship of the pairs are found to be stable. The short-term relationship varies from pair to pair, where some pairs break their cointegrated relationship for some time periods. But generally, most pairs are stable over the short- term as well. The trading strategy generate the highest returns during volatile market conditions and underperforms during positive market conditions with low volatility. The Sharpe ratio is far better than the Index during the whole period.

>Magister Scientiae - MSc / This research undertakes to investigate both long-term and short-term investor overreaction on the JSE Limited (JSE) over the period from 1 January 2002 to 31 December 2009. The period covers the restructuring and reform of the JSE in the early 2000s to the end of global financial market crisis in late 2008/2009, which can be regarded as a complete economic cycle. The performances of the winner and loser portfolios are evaluated by assessing their cumulative abnormal returns (CAR) over a 24-month holding period. The test results show no evidence of mean reversion for winner and loser portfolios formed based on prior returns of 12 months or less. However, test results show evidence of significant mean reversion for the winner and loser portfolios constructed based on their prior 24 months and 36 months returns. In addition, the study reveals that the mean reversion is more significant for longer-formation-period portfolios as well as for longer holding periods. The examination of the cumulative loser-winner spreads obtained from the contrarian portfolios based on the constituents’ prior 24 month and 36 month returns indicates that the contrarian returns increase for portfolios formed between 2004 and 2006, and declines thereafter towards the end of the examination period. The deterioration of contrarian returns coincides with the subprime mortgage crisis in 2007 and the subsequent global financial crisis in 2008. This evidence suggests that the degree of mean reversion on the JSE is positively correlated to the South African business cycle.

The study examined the momentum in the fundamentals of companies over time, and whether the information content in the momentum of the fundamentals improved the understanding of the long-standing price momentum and earnings momentum anomalies on the Johannesburg Stock Exchange (JSE). Fundamental momentum is defined as the difference between the change of a fundamental variable over consecutive time periods. The study included all industrial companies that were listed on the JSE between the period January 1990 and December 2013. The purpose of the study was to investigate whether price momentum or earnings momentum was subsumed by fundamental momentum. Price momentum and earnings momentum are long-standing anomalies that have been widely researched, yet no definitive explanation has been provided in the literature. The objective of the study was to improve the understanding of price momentum and earnings momentum through the analysis of fundamental momentum. The study also provided insight into the persistence of fundamental momentum of earnings. The study tested the profitability of the price momentum, earnings momentum and the fundamental momentum of earnings trading strategies. The research hypotheses were formulated and tested using equal-weighted sort analysis. The sustainability of fundamental momentum of earnings was also analysed. The size and value risk factors were taken into account to ensure that the results were not influenced by such risk factors. The Fama and French three-factor model was employed to test whether the results captured one of these risk effects. The fourth research question investigated whether the fundamental momentum of an underlying component of earnings increased the persistence of the fundamental momentum of future earnings. Earnings were shown to be mean reverting over time, and therefore, the expectation was that positive or negative fundamental momentum of earnings was not sustainable over a prolonged period of time. However, by decomposing earnings into the accrual and cash flow components, and their respective sub-components, the study undertook regression analysis to see whether a specific component of earnings could improve the sustainability of fundamental momentum. The final research question tested whether price momentum and/or earnings momentum was subsumed by fundamental momentum. Two-way analysis was conducted to test whether the strategies captured similar effects. Sort analysis was used by first constructing equal-weighted portfolios based on either price momentum or earnings momentum. Each portfolio was then further subdivided based on the fundamental momentum of earnings. The profitability of the resultant portfolios was then compared with the initial portfolio. The results confirmed that the price momentum and earnings momentum anomalies were present on the JSE for the sample selected for the study. The fundamental momentum of earnings trading strategy was also shown to be a profitable trading strategy for the extreme quintile portfolios. Using the Fama-MacBeth regression methodology, size and value effects were not found to impact the results across all three momentum strategies. A behavioural overreaction or underreaction hypothesis was argued to explain the profitability of the fundamental momentum of earnings strategy. The market was shown to anticipate the earnings surprise that resulted in earnings momentum up to 12 months prior to portfolio formation. Similarly, the market anticipated fundamental momentum of earnings 12 months prior to the earnings announcement. The fundamental momentum of future earnings was shown to be more sustainable when the fundamental momentum of the cash flow component of prior earnings was higher than the fundamental momentum of the accrual component of prior earnings. This result did not give insight into the nominal size effect of the underlying earnings components, rather, it only gave insight into the rates of change of the earnings components. The final result of the study showed that price momentum and fundamental momentum captured different effects. However, the earnings momentum and fundamental momentum results were not as clear cut. Both strategies used a variant of earnings to construct the quintile portfolios and thus it was very plausible that they captured a similar effect. The study contributed to the current literature in a number of ways. A new trading strategy based on the fundamental momentum of earnings was tested. Fundamental momentum of earnings as a trading strategy has yet to be defined; as a result, it has not been researched prior to this study. Given the results, it may be seen as a derivative of earnings momentum. Understanding the sustainability of fundamental momentum of future earnings was also researched. The final contribution of the study was the two-way analysis of price momentum and fundamental momentum, and earnings momentum and fundamental momentum. / Thesis (PhD)--University of Pretoria, 2016. / tm2016 / Financial Management / PhD / Unrestricted

The activity of algorithmic trading is increasing steadily across capital markets due to technological developments. This thesis analyses the common algorithmic intraday trading strategies of momentum, mean reversion, and statistical arbitrage. Conclusions were drawn from a literature review of prior and current research. Algorithmic arbitrage was found to be the most profitable of the three evaluated strategies, because it typically takes place in high frequency trading. Furthermore, this thesis analyses the impact of algorithmic trading on market liquidity and volatility. While the literature mainly agrees that algorithmic trading has a positive effect on liquidity, its impact on volatility is subject to discussion. Algorithmic and high-frequency trading carry risks that will likely lead to new future regulations.:1 INTRODUCTION 1.1 Background 1.2 Problem description and goal of the research 1.3 Structure of the thesis and research questions 2 THEORETICAL FUNDAMENTALS 2.1 Intraday trading 2.1.1 Definition 2.1.2 Characteristics of intraday trading markets 2.1.3 Financial instruments of intraday trading 2.1.4 Goals and profit chances of individual intraday traders 2.2 Algorithmic trading 2.2.1 Algorithm definitions 2.2.2 Algorithmic trading definitions 2.2.3 High-frequency trading 2.2.4 Characteristics of algorithmic trading and high-frequency trading 2.2.5 Trading algorithm characteristics 3 METHODOLOGY 3.1 Data collection 3.2 Data analysis 4 ALGORITHM-BASED INTRADAY TRADING STRATEGIES AND THEIR PROFIT POTENTIAL 4.1 Momentum strategy 4.1.1 Definition and basic principle of the strategy 4.1.2 Underlying theories of the momentum strategy 4.1.3 Selected studies of an algorithmic intraday momentum strategy 4.2 Mean reversion strategy 4.2.1 Definition and basic principle of the strategy 4.2.2 Underlying theories of the mean reversion strategy 4.2.3 Relation of mean reversion and momentum 4.2.4 Selected studies of an algorithmic intraday mean reversion strategy 4.3 Arbitrage strategy 4.3.1 Definition and basic principle of the strategy 4.3.2 Types of Arbitrage 4.3.3 Underlying theories of the arbitrage strategy 4.3.4 Selected studies of an algorithmic intraday statistical arbitrage strategy 4.4 Further trading algorithms and strategy components 4.4.1 Speed Advantage algorithms 4.4.2 Accuracy Advantage Algorithms 5 IMPACT OF ALGORITHMIC TRADING ON MARKET LIQUIDITY AND VOLATILITY 5.1 Market liquidity 5.1.1 Definition 5.1.2 Bid-Ask Spread 5.1.3 Dimensions of liquidity 5.1.4 The impact of algorithmic trading on market liquidity 5.2 Market volatility 5.2.1 Definition and characteristics of volatility 5.2.2 The impact of algorithmic trading on market volatility 6 CONCLUSION AND FUTURE DEVELOPMENTS OF ALGORITHMIC TRADING PUBLICATION BIBLIOGRAPHY DECLARATION OF HONOR

The purpose of this study is to examine whether the returns on the Stockholm Stock Exchange and its different sectors is mean reverting during the period 2003–2019. In addition to the examination of the entire period, the study also examines the periods before, during and after the global financial crisis. Daily, weekly and monthly data is used in combination with three different statistical methods in the form of ADF-tests, KPSS-tests and GPH-tests. The previous research conducted within the area yield different results but the tendency to find support for mean reversion increases during periods of economic uncertainty. The main standpoint used in previous literature and also this study is based on Famas (1970) publication about the efficient market hypothesis (EMH).  The results from the entire period are in line with the EMH where no robust indications for mean reversion is found neither for the specific sectors or the index itself. However, when the different periods are examined, in the period following the global financial crisis the healthcare and real estate sector show signs of mean reversion. In summary, the results show more support for mean reversion when the sectors are examined in relation to the entire Stockholm Stock Exchange. The conclusion based on these results is that an investor, in contrast to the EMH, can use the serial correlation in returns for these sectors as an indicator for when to buy and sell assets during periods as the one following the global financial crisis.

This study examines how investors can take advantage of Black Swan events by applying an investment strategy that involves investing in stocks that have performed badly during Black Swan events. The stocks are chosen from and compared to the Dow Jones Industrial Average Index. The purpose is to find out if the investment strategy has had a higher return than the benchmark index DJIA. The results show that the investment strategy outperforms the DJIA by 111% between the years 2000 to 2020, however, the results show no statistical significance. Beta is used as risk measurement to explain the correlation between the portfolios and the benchmark index by calculating CAPM. Standard deviation is used to calculate the Sharpe ratio and thereby assess a risk-adjusted result.