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61 lines
8.2 KiB
61 lines
8.2 KiB
名称
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供应商集中度动态调整因子
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假设
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供应商集中度（前五大供应商采购额占总采购额比重）的动态变化直接反映企业供应链风险的管控能力。若企业供应商集中度从高位持续回落，意味着其正在主动分散供应链依赖风险，能够有效降低单一供应商违约、提价或断供带来的经营冲击，进而提升盈利稳定性与抗风险能力，这类企业应享有估值溢价，适合建立多头仓位；反之，若供应商集中度从低位持续攀升，企业对少数供应商的依赖度加深，供应链脆弱性上升，经营不确定性增加，适合建立空头仓位。此外，集中度调整的速度与幅度和超额收益呈正相关，快速且合理的分散调整比缓慢调整更具信号价值。
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实施方案
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计算核心指标：基于企业采购数据，测算供应商集中度比率（CR5 = 前五大供应商采购金额 / 总采购金额）；
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时序趋势分析：使用时序趋势算子（ts_trend）拟合过去 12 个月 CR5 的变化斜率，区分 “持续下降（斜率为负且绝对值大于阈值）”“持续上升（斜率为正且绝对值大于阈值）”“平稳波动” 三类标的；
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规模与行业校准：将 CR5 除以企业总采购额的对数以消除规模影响，同时计算标的 CR5 与行业均值的偏离度；
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构建多空策略：做多 “CR5 持续下降 + 当前 CR5 低于行业均值” 的标的；做空 “CR5 持续上升 + 当前 CR5 高于行业均值” 的标的；剔除 CR5 平稳波动且偏离行业均值较小的标的以降低噪声。
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阿尔法因子优化建议
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引入行业差异化阈值：不同行业的供应商集中度基准值差异显著（如半导体行业核心物料供应商集中度天然偏高，快消品行业集中度偏低），建议采用行业分位数算子替代固定阈值，在行业内部分层判断集中度调整的合理性；
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叠加供应商质量验证：整合供应商信用评级、合作年限等数据，当集中度下降伴随 “新增供应商信用评级高于原有供应商” 时，强化多头信号权重；若集中度上升源于 “优质供应商排他性合作”，则弱化空头信号；
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事件驱动权重调整：运用事件触发算子，在行业性供应链危机（如原材料涨价潮、地缘政治导致的物料断供）发生时，放大该因子的配置权重，捕捉危机期间供应链稳健企业的超额收益；
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分档加权优化：采用分层算子将集中度调整幅度分为 “大幅调整”“中度调整”“小幅调整” 三档，针对不同档位设置差异化仓位权重，提升策略的风险收益比。
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Name
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Dynamic Adjustment Factor of Supplier Concentration
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Hypothesis
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The dynamic change in supplier concentration (measured by the proportion of purchases from the top 5 suppliers to total purchases) directly reflects an enterprise's ability to control supply chain risks. If a company's supplier concentration continues to decline from a high level, it indicates that it is actively diversifying supply chain dependence risks, which can effectively reduce operational shocks caused by default, price increase or supply disruption of a single supplier, thereby improving profit stability and risk resistance. Such enterprises deserve a valuation premium and are suitable for establishing long positions. Conversely, if supplier concentration continues to rise from a low level, the enterprise's dependence on a few suppliers deepens, supply chain vulnerability increases, and operational uncertainty rises, making it suitable for establishing short positions. In addition, the speed and magnitude of concentration adjustment are positively correlated with excess returns—fast and reasonable diversification adjustments have higher signal value than slow adjustments.
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Implementation Plan
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Calculate core indicators: Based on enterprise procurement data, measure the supplier concentration ratio (CR5 = purchase amount from top 5 suppliers / total purchase amount);
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Time-series trend analysis: Use the time-series trend operator (ts_trend) to fit the change slope of CR5 over the past 12 months, and classify targets into three categories: "continuous decline (negative slope with absolute value greater than threshold)", "continuous rise (positive slope with absolute value greater than threshold)", and "stable fluctuation";
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Scale and industry calibration: Divide CR5 by the logarithm of the enterprise's total purchase amount to eliminate scale effects, and calculate the deviation of the target's CR5 from the industry average;
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Construct long-short strategy: Establish long positions on targets with "continuously declining CR5 + current CR5 below industry average"; establish short positions on targets with "continuously rising CR5 + current CR5 above industry average"; exclude targets with stable CR5 fluctuation and small deviation from industry average to reduce noise.
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Alpha Factor Optimization Suggestions
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Introduce industry-differentiated thresholds: There are significant differences in the benchmark values of supplier concentration across industries (e.g., the concentration of core material suppliers in the semiconductor industry is naturally high, while that in the consumer goods industry is low). It is recommended to use the industry quantile operator instead of fixed thresholds to judge the rationality of concentration adjustment by stratification within the industry;
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Superimpose supplier quality verification: Integrate data such as supplier credit ratings and cooperation years. When the decline in concentration is accompanied by "the credit rating of new suppliers being higher than that of original suppliers", strengthen the weight of long signals; if the rise in concentration stems from "exclusive cooperation with high-quality suppliers", weaken the short signals;
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Event-driven weight adjustment: Use the event trigger operator to increase the allocation weight of this factor during industry-wide supply chain crises (such as raw material price surges, material supply disruptions caused by geopolitics), capturing excess returns of supply chain-robust enterprises during crises;
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Tiered weighting optimization: Use the stratification operator to divide the concentration adjustment range into three tiers: "significant adjustment", "moderate adjustment" and "minor adjustment", and set differentiated position weights for different tiers to improve the risk-return ratio of the strategy.
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*=========================================================================================*
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输出格式：
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输出必须是且仅是纯文本。
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每一行是一个完整、独立、语法正确的WebSim表达式。
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严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。
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===================== !!! 重点(输出方式) !!! =====================
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现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。
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不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合
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**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不需要赋值, 不要解释, 不需要序号, 也不要输出多余的东西)：
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表达式
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表达式
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表达式
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...
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表达式
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=================================================================
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重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。
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以下是我的账号有权限使用的操作符, 请严格按照操作符, 以及我提供的数据集, 进行生成,组合 30 个alpha：
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不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合
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=================================================================
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ts_product ts_zscore ts_mean ts_scale add sign subtract ts_delta ts_rank greater ts_av_diff ts_quantile ts_count_nans ts_covariance
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ts_arg_min divide ts_corr multiply if_else ts_sum ts_delay group_zscore ts_arg_max ts_std_de ts_backfill
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以上这些操作符不能传入事件类型的数据集, 只能传入时间序列数据集, 不能传入事件数据,不能传入事件数据,不能传入事件数据 |