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              <text>This study expands upon the work of Barberis (2012) which discusses the application of Tversky and Kahneman’s updated model of prospect theory in 1992 (cumulative prosepect theory) to the area of repeated bets with win probabilities of close to 0.5, in contexts such as casinos or financial markets. In particular, we look to explore the idea of a ’time inconsistency’ that is abundant in many strategic situations evaluated using cumulative prospect theory. What this refers to is the phenomenon of an agent deciding what their optimal strategy is before entering the situation but then following a different plan of action at future time periods. We explore the impacts of moving away from simple bets by changing some of the underlying principles, such as the effects of generalising the win probability, p, or introducing a random shock to the reward at a random point in time. This allows us to connect Barberis’ work to areas beyond just a casino, such as prices of securities in financial markets. We will do this by exploring the effects on a two-period binomial model but which can be extended.</text>
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              <text>Jy Naha, Decision Theory, Mathematical Finance, Cumulative Prospect Theory, Gambling, Trading, Financial Markets, Statistics, Behavioural Economics, Utility Theory, Game Theory, Optimisation</text>
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                <text>The Time Inconsistency Problem</text>
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              <text>The Russia--Ukraine war disrupted global food commodity markets, raising concerns over food prices and food security across Africa. This study examines whether countries with greater pre-war dependence on cereal imports from Russia and Ukraine experienced larger increases in domestic food inflation following the outbreak of the conflict, and whether transmission varied with economic development.  Using a monthly panel of 25 African economies from January 2017 to September 2025, the study employs a Difference-in-Differences framework exploiting cross-country variation in pre-war import dependence. Country and month fixed effects account for unobserved heterogeneity and common global shocks, while exchange-rate movements are included as a control. Robustness is assessed using alternative treatment definitions, fixed-effects specifications, placebo tests and leave-one-country-out estimation.  The baseline results provide limited evidence that pre-war dependence on Russian and Ukrainian cereal imports systematically increased domestic food inflation following the outbreak of the war. However, substantial heterogeneity emerges across countries. Higher economic development is associated with significantly smaller increases in food inflation. Further analysis indicates that supplier concentration and domestic cereal production capacity help explain this resilience. Overall, the findings suggest that structural characteristics, rather than import dependence alone, shape countries' resilience to global food commodity shocks.</text>
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                <text>The Transmission of Global Food Commodity Shocks to Food Inflation in Africa: Evidence from the Russia–Ukraine War</text>
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                <text>Daniel Whitehouse, Prof Natalie Chen</text>
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              <text>Traditionally, academics have suggested that generating excess returns off timing the market, i.e. buying or selling financial products off frequent predictions requires maintaining an unreasonably high level of predictive accuracy. However, recent advancements in machine learning have motivated the adoption of predictive algorithms in financial markets. This highlights the importance of the question: How accurate do predictive algorithms have to be "useful"? To answer this, we study 2 problems related to the value of predictive accuracy in directional models for trading. We obtain results on the relationship between predictive accuracy and returns, and results on quantifying thresholds for when a predictive signal provides significant edge. </text>
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