第 1 / 7 页
非代码面试题
显示 20 / 124 道匹配题目
答题状态:未尝试未正确已正确
ID题目领域难度题型进度权限
2671Effective Breadth Under Common Factor CorrelationA researcher tracks 100 cross-sectional alpha sleeves whose pairwise correlation is approximately 0.20. Using the rough equal-correlation breadth proxy n eff =n/(1+(n-1) ), what is the effective number of independent bets?机器学习简单数值题未尝试免费2672Autocorrelation-Corrected Sample SizeA monthly feature is observed for 60 months and behaves roughly like an AR(1) series with lag-1 autocorrelation =0.6. Using the heuristic n eff \approx n(1- )/(1+ ), what is the effective sample size?机器学习简单数值题未尝试面试订阅2673Average Edge Across Opposing RegimesA signal earns +6 bps on 70% of days in calm markets and -10 bps on 30% of days in stressed markets. What is its unconditional average daily edge in bps?机器学习中等数值题未尝试面试订阅2674Precision of a Rare Alpha Event DetectorOnly 2% of days contain a true dislocation worth trading. A classifier catches 65% of those days but fires falsely on 4% of normal days. What is the precision of a positive alert?机器学习中等derivation未尝试面试订阅2675Break-Even Hit Rate After Trading CostsA directional model earns +1 unit on a correct trade and -1 unit on an incorrect trade before costs. Each round trip also pays a cost of 0.08 units regardless of outcome. What hit rate p makes expected net PnL zero?机器学习困难derivation未尝试面试订阅2676A Tiny Tail Probability Can Dominate Average PnLA strategy makes +0.04% on 98% of days and loses -2.5% on the remaining 2% of days. What is the unconditional average daily return?机器学习简单数值题未尝试免费2677Half-Life Discounting of Old Regime DataSuppose predictive relevance decays with a half-life of 6 months. Relative to current-regime data, what weight should you attach to observations that are 18 months old under this exponential-decay heuristic?机器学习中等derivation未尝试面试订阅2678Net Signal-to-Cost RatioA strategy's gross expected edge is 5 bps per trade, but execution-cost uncertainty has a standard deviation of 12 bps per trade. What is the gross-edge-to-cost-noise ratio?机器学习中等数值题未尝试面试订阅2679Why Hundreds of Stocks Do Not Mean Hundreds of Independent LabelsWhy does a daily cross-sectional equity sample with hundreds of names still provide much less information than its row count suggests?机器学习中等essay未尝试面试订阅2680Why Low R-Squared Can Still Be Valuable Yet Hard to VerifyWhy can a signal with tiny explanatory power still be economically useful, while also being unusually hard to validate convincingly?机器学习困难essay未尝试面试订阅2681Why Live Deployment Changes the Labeling EnvironmentWhy can a model that looked predictive in backtests become less predictive once a desk actually starts trading on it?机器学习简单essay未尝试免费2682Why Crowding Erodes Published Signals FastestWhy do signals that are easiest to explain and copy often decay faster after publication than more fragile niche edges?机器学习简单essay未尝试面试订阅2683Why Long Training Windows Can Learn the Wrong WorldWhy can adding more historical years lower estimation variance and yet make a finance model worse?机器学习中等essay未尝试面试订阅2684Why Short Windows Adapt but Also WhipsawWhy does a short rolling window often react faster to new regimes while simultaneously making parameter estimates much less stable?机器学习困难essay未尝试面试订阅2685Why Crisis Prediction Suffers From Tiny Relevant Sample SizeWhy does a century of daily data still leave very little effective evidence for training a model about true crisis behavior?机器学习困难essay未尝试面试订阅2686Why Correlation Spikes Break Signal Combining RulesWhy can a portfolio of seemingly diversified alpha signals stop diversifying exactly when markets become stressed?机器学习简单essay未尝试免费2687Why Benchmark Stability Does Not Guarantee Label StabilityWhy can a model's benchmark-relative performance appear stable even while the mapping from features to returns is drifting underneath?机器学习中等essay未尝试面试订阅2688Why Transaction Costs Are Model Uncertainty TooWhy is it wrong to treat trading costs as a fixed deduction after the predictive model has already been validated?机器学习中等essay未尝试面试订阅2689Why Adaptive Opponents Break Stationary Learning AssumptionsWhy is finance especially hostile to the idea that the data-generating process will sit still while your model learns it?机器学习困难essay未尝试面试订阅2690Why Fast Strategies Are More Sensitive to Small Modeling ErrorsWhy can a tiny forecast error or latency miss matter much more for a very fast strategy than for a slow rebalancing signal?机器学习困难essay未尝试面试订阅