Is Every Story Political?

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Is Every Story Political?

There’s a hot topic that comes around the internet every once in awhile which debates whether all stories are inherently political. Some say that stories should never be political, since the story would then implode due to the writer’s biases. Others say stories must be political, and therefore writers should embrace the political nature of what they tell. As an experienced writer, I figured I’ll throw my hat in the ring when it comes to this topic.

Decision Trees for Intuitive Intraday Trading Strategies

This research paper aims to investigate the efficacy of decision trees in constructing intraday trading strategies using existing technical indicators for individual equities in the NIFTY50 index. Unlike conventional methods that rely on a fixed set of rules based on combinations of technical indicators developed by a human trader through their analysis, the proposed approach leverages decision trees to create unique trading rules for each stock, potentially enhancing trading performance and saving time. By extensively backtesting the strategy for each stock, a trader can determine whether to employ the rules generated by the decision tree for that specific stock. While this method does not guarantee success for every stock, decision treebased strategies outperform the simple buy-and-hold strategy for many stocks. The results highlight the proficiency of decision trees as a valuable tool for enhancing intraday trading performance on a stock-bystock basis and could be of interest to traders seeking to improve their trading strategies.

Stochastic arbitrage with market index options

Opportunities for stochastic arbitrage in an options market arise when it is possible to construct a portfolio of options which provides a positive option premium and which, when combined with a direct investment in the underlying asset, generates a payoff which stochastically dominates the payoff from the direct investment in the underlying asset. We provide linear and mixed-integer linear programs for computing the stochastic arbitrage opportunity providing the maximum option premium to an investor. We apply our programs to 18 years of data on monthly put and call options on the Standard & Poors 500 index, confining attention to options with moderate moneyness, and using two specifications of the underlying asset return distribution, one symmetric and one skewed. The pricing of market index options with moderate moneyness appears to be broadly consistent with our skewed specification of market returns.

A K-means Algorithm for Financial Market Risk Forecasting

Financial market risk forecasting involves applying mathematical models, historical data analysis and statistical methods to estimate the impact of future market movements on investments. This process is crucial for investors to develop strategies, financial institutions to manage assets and regulators to formulate policy. In today's society, there are problems of high error rate and low precision in financial market risk prediction, which greatly affect the accuracy of financial market risk prediction. K-means algorithm in machine learning is an effective risk prediction technique for financial market. This study uses K-means algorithm to develop a financial market risk prediction system, which significantly improves the accuracy and efficiency of financial market risk prediction. Ultimately, the outcomes of the experiments confirm that the Kmeans algorithm operates with user-friendly simplicity and achieves a 94.61% accuracy rate.