发表机构
Graph AI LLC（Graph AI有限责任公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MarketRank 利用马尔可夫链平稳概率对股票按估计资金流排序，并通过拓扑景观视图可视化资金集中与变化，但得分不预测短期收益。
AI 中文摘要
每个交易日，资金从一些股票流出并流入其他股票。MarketRank 询问资金集中在哪里，以及这种集中如何随时间变化。它将市场视为一个马尔可夫链，其状态为股票。从一只股票转移到另一只股票的概率是前一只股票流出的美元中流向第二只股票的比例，而一只股票的 MarketRank 得分是该链中该状态的长期（平稳）概率，通过稀疏转移矩阵上的阻尼幂迭代计算。PageRank 使用相同的思路对网页进行排序，但使用链接而非美元。谁卖出什么以买入什么并非公开信息，因此资金流通过相关性倾斜重力模型从每日价格和成交量中估计，并在确定性并行 C++ 引擎中实现。在包含 10K 美国股票和 ETF 的股票池中，排名由大型半导体和技术公司领衔，且估计的资金流在行业内发生的频率高于随机水平。然而，得分水平并不能预测次日收益：它显示资金所在位置，而非价格下一步走向。3D 景观视图 Fluxscape 将排名转化为地形：近期资金流图的 Louvain 社区（在每个柱上重新聚类）成为区域，通过沿广义希尔伯特曲线的递归谱二分排序，经反距离加权插值，通过质心 Voronoi 式松弛平滑，并将投资组合持仓固定在其精确值，渲染为 Gouraud 着色网格。该表面用于读取趋势；精确排名表仍为参考。实际问题在于 MarketRank 能否改善真实投资组合操作，我们通过本研究固定的测试来回答。
英文摘要
Every trading day, money leaves some stocks and moves into others. MarketRank asks where that money concentrates and how the concentration shifts over time. It treats the market as a Markov chain whose states are stocks. The chance of moving from one stock to another is the share of the first stock's outgoing dollars that goes to the second, and a stock's MarketRank score is its long run (stationary) probability in that chain, computed by damped power iteration on a sparse transition matrix. PageRank ranks web pages with the same idea, using links instead of dollars. Who sold what to buy what is not public, so the flows are estimated from daily prices and volumes with a correlation tilted gravity model, in a deterministic parallel C++ engine. On a universe of 10K US stocks and ETFs, the ranking is led by the large semiconductor and technology names, and estimated flows stay within a sector more often than chance. The level of the score, however, does not predict next day returns: it shows where money sits, not where prices go next. A 3D landscape view, Fluxscape, turns the ranking into terrain: Louvain communities of the recent flow graph, reclustered on every bar, become territories, ordered by recursive spectral bisection along a generalized Hilbert curve, interpolated by inverse distance weighting, smoothed by a centroidal Voronoi style relaxation with portfolio holdings pinned to their exact values, and rendered as a Gouraud shaded mesh. The surface is for reading trends; the exact ranked table remains the reference. The practical question is whether MarketRank can improve real portfolio moves, and we answer it with tests fixed in this study.
