智能投顧——大數據智能驅動投顧創新

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智能投顧——大數據智能驅動投顧創新

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內容描述

本書圍繞智能投顧的前世與今生、概念與理論、商業模式與技術、智能投顧的監管、生態以及未來發展趨勢進行歸納與分析。全書共9章。第1章闡釋智能投顧的誕生，涉及金融科技概念、人工智能概念等；第2章介紹智能投顧涉及的技術、覆蓋範圍等；第3章闡釋智能投顧的概念及投資流程；第4～6章從微觀角度介紹智能投顧的商業模式與技術支持，涉及智能投顧的服務對象、投資標的、推廣，傳統經濟學、統計學、行為學背景下智能投顧中的投資理論，以及人工智能和金融科技背景下智能投顧的前沿金融智能技術；第7章和第8章從宏觀角度介紹智能投顧的政策支持與生態系統；第9章對智能投顧做出總結和展望。 本書內容豐富，觀點超然，案例典型，結構清晰，適合對智能投顧感興趣的從業者、研究人員、愛好者閱讀。

目錄大綱

目錄
第1章智能投顧的誕生
案例：Betterment的誕生·················2
1.1 金融科技的興盛·····················3
1.1.1 金融科技的概念················4
1.1.2 金融科技的全球發展態勢·····5
1.1.3 金融科技在中國················9
1.2 人工智能的崛起···················13
1.2.1 人工智能的概念···············14
1.2.2 人工智能的全球發展態勢····14
1.2.3 人工智能在中國···············18
1.2.4 人工智能投融資狀況··········19
1.3 智能投顧應運而生················20
1.4 本書概覽····························21
本章小結··································22
參考文獻··································22
第2章智能投顧的歷史與發展現狀
案例：中美智能投顧發展比較········25
2.1 智能投顧的發展歷史·············27
2.1.1 標籤過濾階段··················29
2.1.2 用戶風險承受能力測試階段···29
2.1.3 個性化投資組合推薦階段····30
2.1.4 全自動智能投顧階段··········31
2.2 智能投顧產業全球的發展現狀··31
2.2.1 全球智能投顧發展數據總覽···31
2.2.2 美洲······························33
2.2.3 歐洲······························37
2.2.4 亞洲······························39
2.3 智能投顧在中國···················40
2.3.1 中國的智能投顧平台現狀····40
2.3.2 中國智能投顧行業發展現狀···42
2.3.3 中國智能投顧的發展機遇····47
本章小結··································49
參考文獻··································50
第3章智能投顧的概念與投資流程
案例：Betterment的投資流程·········53
3.1 智能投顧的概念···················54
3.1.1 傳統的投資顧問···············54
3.1.2 智能投資顧問··················55
3.1.3 智能投顧的作用···············58
3.2 智能投顧的投資流程·············60
3.2.1 客戶分析························61
3.2.2 大類資產配置··················63
3.2.3 投資組合選擇··················65
3.2.4 交易執行························66
3.2.5 投資組合再選擇···············67
3.2.6 稅負管理························69
3.2.7 組合分析························70
本章小結··································70
參考文獻··································71
第4章智能投顧的商業模式
案例：Betterment的商業模式·········73
4.1 智能投顧的目標客戶·············74
4.1.1 中產及中產以下收入人群····75
4.1.2 金融專業人士··················77
4.2 智能投顧的投資標的·············78
4.2.1 常見的投資標的···············78
4.2.2 新興的投資標的···············81
4.3 智能投顧的業務模式·············82
4.3.1 全自動與半自動投顧··········83
4.3.2 投資平台與相關智能服務····87
4.3.3 大類資產配置、投資策略與社交跟投等··················88
4.4 智能投顧的營銷模式·············89
4.4.1 打造明星產品··················90
4.4.2 分級客戶群匹配服務，提高服務水平··················91
4.4.3 利用大數據進行精準營銷····91
4.5 智能投顧的盈利模式·············92
4.5.1 前端收費模式··················93
4.5.2 後端盈利模式··················94
本章小結··································94
參考文獻··································95
第5章智能投顧理論入門
案例：馬科維茨理論的產生···········98
5.1 馬科維茨投資組合理論··········99
5.1.1 現代投資組合理論概述·······99
5.1.2 投資組合的可行集和有效集·························100
5.1.3 均值方差分析方法···········101
5.2 資本資產定價模型···············102
5.2.1 CAPM假設條件·············103
5.2.2 分離定理······················104
5.2.3 資本市場線與證券市場線··106
5.3 套利定價理論·····················107
5.3.1 APT概述······················107
5.3.2 因素模型······················108
5.3.3 套利定價理論················110
5.4 行為金融學························111
5.4.1 行為金融學的概念···········112
5.4.2 行為金融學的應用實例·····114
5.5 投資過程評價·····················115
5.5.1 收益類指標···················115
5.5.2 風險類指標···················116
本章小結·································118
參考文獻·································118
第6章智能投顧中的人工智能技術
案例：嘉信智能理財···················121
6.1 大數據融合技術··················121
6.1.1 大數據融合的背景···········122
6.1.2 多源數據融合技術···········123
6.2 投資客戶偏好畫像···············125
6.2.1 用戶畫像的定義·············126
6.2.2 用戶畫像的生成流程········128
6.2.3 客戶畫像的核心工作——打標籤·························129
6.3 量化投資技術·····················130
6.3.1 量化投資的概念·············130
6.3.2 機器學習應用到量化投資中·························131
6.3.3 深度學習應用到量化投資中·························135
6.4 投資組合配置技術···············138
6.4.1 問題定義······················138
6.4.2 二次規劃······················140
6.4.3 強化學習······················142
6.5 投資風險控制技術···············144
6.5.1 風險的定義···················145
6.5.2 風險測量—方差···········145
6.5.3 風險測量—VaR ···········146
6.6 NLP在智能金融領域中的應用································147
6.6.1 智能金融領域現有需求·····148
6.6.2 NLP的金融應用場景·······149
6.6.3 NLP在金融領域應用案例····························153
本章小結·································155
參考文獻·································156
第7章智能投顧的風險、監管和政策支持
案例：真假智能投顧···················160
7.1 中國智能投顧的風險············161
7.1.1 智能投顧的風險構成········161
7.1.2 智能投顧的風險預警········163
7.1.3 智能投顧平台的風險指數體系······················164
7.2 智能投顧的監管難點············167
7.2.1 賬戶實際控制人認定困難··168
7.2.2 一致行動人···················168
7.2.3 監管法律體係不完善········168
7.2.4 智能投顧行為邊界判定·····169
7.3 海外智能投顧的監管經驗······170
7.3.1 美國的智能投顧監管········170
7.3.2 英國的智能投顧監管：監管沙盒······················171
7.3.3 澳洲的智能投顧監管········177
7.3.4 新加坡的智能投顧監管·····180
7.3.5 不同國家智能投顧的監管體係比較······················183
7.4 中國智能投顧的監管體系······185
7.4.1 智能投顧監管框架···········185
7.4.2 智能投顧的監管路徑········187
7.5 中國智能投顧的政府支持······192
7.5.1 智能投顧發展過程中的政府政策······················192
7.5.2 《資管新規》對智能投顧的影響···················193
7.5.3 學界對智能投顧的支持·····195
本章小結·································196
參考文獻·································196
第8章智能投顧的生態系統
案例：新加坡的金融科技生態系統···199
8.1 金融科技生態系統···············200
8.2 智能投顧生態系統的概念與構成································203
8.2.1 智能投顧生態系統的概念··203
8.2.2 智能投顧生態系統的生態主體······················206
8.2.3 智能投顧生態系統中的產業鏈關係···················213
8.3 智能投顧生態系統的維護和優化································217
8.3.1 國內智能投顧生態系統存在的缺陷···················217
8.3.2 智能投顧生態系統的優化辦法······················218
本章小結·································220
參考文獻·································220
第9章智能投顧的未來
案例：螞蟻集團—從FinTech到TechFin，BASIC科技戰略打造折疊式生態體系························223
9.1 人工智能的發展新階段·········223
9.2 智能投顧大腦·····················225
9.3 AI驅動的理性機器經濟人······227
9.4 智能投顧的未來發展趨勢······229
9.4.1 商業模式發展趨勢···········229
9.4.2 智能投顧技術發展趨勢·····230
9.4.3 智能投顧監管發展趨勢·····231
9.5 開放銀行與虛擬銀行············232
9.5.1 開放銀行······················232
9.5.2 虚拟银行 ······················233
本章小結·································234
參考文獻·································234

作者介紹

鄭小林，博士，浙江大學計算機學院教授，人工智能研究所副所長，斯坦福大學訪問學者，阿里巴巴青年學者理事會理事，入選浙江省151人才工程，擔任杭州金智塔科技有限公司首席科學家。主持國家、省部級項目和課題10多項；以第一或通訊作者發表學術論文50多篇；作為主要骨幹獲得2020年“CCF科學技術獎”卓越獎（一等獎），2018年教育部科技進步獎二等獎，2014年浙江省科技進步獎一等獎；榮獲2009年阿里巴巴青年學者計劃優秀學者，2009 IBM中國優秀教師獎教金。
賁聖林，博士，先後獲得清華大學工程學士學位、中國人民大學企業管理碩士學位和美國普渡大學經濟學博士學位。現任浙江大學國際聯合商學院院長、互聯網金融研究院院長、管理學院教授。兼任中國人民大學國際貨幣研究所聯席所長，全國工商聯國際委員會委員，中華海外聯誼會常務理事，中央統戰部黨外知識分子建言獻策專家組成員，浙江省政協常委、經濟委員會副主任，浙江省人民政府參事，浙江互聯網金融聯合會聯合主席，廣東金融專家顧問委員會顧問委員等。