Suchergebnisse

AbstractBlackRock, the largest US investment management firm, has started to replace human stock‐pickers with the full automated investment program, based on self‐learning artificial intelligence algorithms. This is the first high‐profile case in the financial industry where artificial intelligence replaces human discretion. The advanced version of BlackRock Robo‐Advisor can also be customized to perform within the strategic planning framework. Human discretionary decision‐making jobs are at risk of being replaced by machines.

AbstractDeflation presents special challenges to central banking, as traditional monetary policy tools are highly inefficient in dealing with deflationary pressures. In this case, the Federal Reserve must use alternative monetary policy tools that are specially designed to artificially boost asset prices through "printing press" or currency manipulation. Unfortunately, these alternative monetary policy tools create unintended political, geopolitical, and social consequences that overreach into the direct responsibilities of other branches of government. Thus, the government must be able to influence Federal Open Market Committee decisions that potentially affect (or contradict) U.S. foreign policy, U.S. trade policy, U.S. dollar policy, and deliberate domestic/global wealth distribution policies.

AbstractThis study proposes an alternative Data Envelopment Analysis ranking model to evaluate the relative performance efficiency of commodity‐trading advisors. I measure the performance efficiency using the decision‐making process quality/trading skills framework and depart from the traditional risk–return framework. The Data Envelopment Analysis rankings produced some interesting results. First, similarly to the previous studies, I successfully isolated two 'superstar' commodity‐trading advisors with the highest Sharpe ratios as the Grade A commodity‐trading advisors. However, as an improvement over the similar studies that used the traditional risk–return framework, I also isolated two commodity‐trading advisors with average and below‐average Sharpe ratios as Grade A commodity‐trading advisors.

PurposeThis study is motivated in part by the fact that the unfolding 2022 bear market, which has reached the −25% drawdown, has not been preceded by the inverted 10Y-3 m spread or an inverted near-term forward spread.Design/methodology/approachThe authors develop a three-factor probit model to predict/explain the deep stock market drawdowns, which the authors define as the drawdowns in excess of 20%.FindingsThe study results show that (1) the rising credit risk predicts a deep drawdown about a year in advance and (2) the monetary policy easing precedes an imminent drawdown below the 20% threshold.Originality/valueThis study three-factor probit model shows adaptability beyond the typical recessionary bear market and predicts/explains the liquidity-based selloffs, like the 2022 and possibly the 1987 deep drawdowns.