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4451Rank Or Score BlendOne signal has stable rank ordering but erratic absolute scale; another has meaningful scale but occasional outliers. When can a rank-based combination be safer than a raw-score combination?机器学习中等essay未尝试面试订阅4452One Loud SignalA new signal has the highest standalone Sharpe in-sample, but it is unstable and highly correlated with existing signals. Why can a shrunken combination be wiser than giving it dominant weight?机器学习中等essay未尝试面试订阅4453Turnover-Aware BlendWhy might a slightly weaker but slower-moving signal deserve positive weight in a production blend?机器学习中等essay未尝试面试订阅4454Equal Weight TrapWhy can equal-weighting many correlated alphas fail to deliver the diversification that the count of signals seems to promise?机器学习中等essay未尝试面试订阅4455Meta-Model Or Handcrafted BlendWhen is a simple handcrafted blend preferable to fitting a flexible meta-model on top of several signals?机器学习中等essay未尝试面试订阅4456Higher CorrelationIf pairwise correlation between signals rises while their standalone quality stays unchanged, what usually happens to the diversification benefit of combining them?机器学习中等essay未尝试面试订阅4457Weight InstabilityIf estimated optimal combination weights jump around from month to month, what is the usual case for shrinkage?机器学习中等essay未尝试面试订阅4458More Signals, Same DataIf you keep adding candidate signals without increasing data length, what often happens to the reliability of estimated combination weights?机器学习中等essay未尝试面试订阅4459Score Scale DriftIf one signal's score scale drifts over time while another remains stable, what usually happens to a fixed raw-score blend?机器学习中等essay未尝试面试订阅4460Slow Signal WeightIf transaction costs rise materially, what usually happens to the appeal of slower-moving signals in the blend?机器学习中等essay未尝试面试订阅4461Before CombiningBefore combining several signals, what should you check first besides each signal's standalone Sharpe?机器学习中等essay未尝试面试订阅4462Before Optimizing WeightsWhat should you inspect first before trusting an optimizer's exact signal weights?机器学习中等essay未尝试面试订阅4463Before Using Raw ScoresWhat is the first comparability question before blending raw signal scores?机器学习中等essay未尝试面试订阅4464Before Adding Meta-ModelingBefore fitting a meta-model on top of several signals, what is the first data question you should ask?机器学习中等essay未尝试面试订阅4465Before Declaring DiversificationWhat should you check first before saying that adding five more signals makes the ensemble diversified?机器学习中等essay未尝试面试订阅4466Risk-Neutral Probability And No-Arbitrage 1In a one-period binomial model, S0=100, Su=120, Sd=90, and the simple risk-free rate is 0. Compute the risk-neutral probability of the up state, and state whether the no-arbitrage condition holds.数理金融简单数值题未尝试面试订阅4467Risk-Neutral Probability And No-Arbitrage 2In a one-period binomial model, S0=50, Su=65, Sd=45, and the simple risk-free rate is 0.05. Compute the risk-neutral probability of the up state, and state whether the no-arbitrage condition holds.数理金融简单数值题未尝试面试订阅4471State-Price Density Solve 1A stock and bond trade in a one-period two-state model with S0=100, Su=120, Sd=80, and gross risk-free return 1. Solve for the state-price densities λu and λd.数理金融中等数值题未尝试面试订阅4472State-Price Density Solve 2A stock and bond trade in a one-period two-state model with S0=50, Su=62, Sd=42, and gross risk-free return 1.02. Solve for the state-price densities λu and λd.数理金融中等数值题未尝试面试订阅4476Convex-Hull No-Arbitrage Test 1A one-period stock has S0=100, future states 118, 94, and 76, and risk-free rate 0. Use the discounted-price convex-hull test to decide whether an arbitrage-free pricing measure can exist.数理金融中等数值题未尝试面试订阅