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    ๋ชจ๋ฐ”์ผ ๋งค์ฒด์˜ ์ˆ˜์ต ์ตœ์ ํ™”๋ฅผ ์œ„ํ•œ ๋””์Šคํ”Œ๋ ˆ์ด ๊ด‘๊ณ  ์š”์†Œ ๋ฐ ์›Œํ„ฐํด ์ž…์ฐฐ ์ „๋žต ํ‰๊ฐ€

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2020. 8. ์œค๋ช…ํ™˜.Advertising revenue has become an important revenue source for mobile publishers, along with in-app purchase. Based on empirical data and academic methodology, this study attempted to solve two key problems that mobile publishers face when trying to maximize advertising revenue. This study analyzed transaction history data of mobile advertising from AD(x) Inc., a company that provides services to optimize ad revenue for mobile publishers by operating multiple ad networks simultaneously, including Google AdMob and Facebook Audience Network. The first problem mobile publishers face when trying to gain revenue through advertising is determining the optimal ad position and ad format for the service UX of mobile publishers. To provide guidelines for the first decision, this study analyzed characteristics of mobile advertising, including native ads and rewarded video ads, which have been relatively recently introduced. As a result, in addition to various ad factors defined by previous research through traditional advertising media, three new ad factors were summarized: ad density, disclosure position, and disclosure method. Moreover, the relationships among the three new derived ad factors, ad revenue, and ad effectiveness were analyzed. First, in relation to ad density, which is the proportion of an advertisements physical area relative to the full-screen area, the higher the ad density, the higher both the ad revenue and advertising effectiveness. On the other hand, among advertisements with similar ad density, there was a difference in ad revenue and advertising effectiveness according to ad format. Among advertisements with low ad density, native banner ads showed higher ad revenue and advertising effectiveness than banner ads. Among advertisements with high ad density, rewarded video ads showed the highest ad revenue, and interstitial ads showed the highest advertising effectiveness. As for the second new ad factor, disclosure position, the effectiveness of advertisements displayed at the top of the screen was higher in the PC web environment, but advertisements displayed at the bottom of the screen in the mobile environment were higher in terms of ad revenue and advertising effectiveness. Lastly, in the analysis of the third new ad factor, disclosure method, advertisements with the same ad format as native ads were classified in three categories, based on their development by mobile publishers: Separated area, List UI, and Pop-up. This study analyzed the relationship between disclosure method, ad revenue, and advertising effectiveness. The results showed that the highest ad revenue and advertising effectiveness were found in the Pop-up disclosure method. The second problem that mobile publishers face after determining ad position and ad format is the optimization of waterfall settings such as the priority and reserve prices of each ad network to maximize ad revenue when mobile advertising is served from multiple ad networks. On the other hand, between ad networks and mobile publishers, there is information asymmetry. Hence, ad networks have more information, so this study proposed a reserve price strategy for the operation of waterfall bidding among multiple ad networks to maximize ad revenue, even under information asymmetry. First, a demand curve-based model was designed to explain the loss of ad revenue when a mobile publisher sells its ad inventory at a non-optimized price using waterfall bidding. In addition, sensitivity analysis was conducted to show that the proposed model performs better than the companys existing bidding strategy. Moreover, this model enabled mobile publishers to have better performance with independent correlation, not a positive correlation of ad networks bid prices. Therefore, mobile publishers can use the key finding that the proposed model is more effective in reducing expected advertising losses under information asymmetry. In addition, it was found that performance improved to a greater extent when ad networks have less bid price similarity. This study provides guidelines that can be utilized not only in an academic sense but also in a real business environment. Standardized knowledge for small- and medium-sized mobile publishers, in particular, which have a relatively high ad network dependency, is suggested to improve their understanding of ad network usage and to establish optimized advertising operation policies.๊ด‘๊ณ  ์ˆ˜์ต์€ ๋ชจ๋ฐ”์ผ ๋งค์ฒด์—๊ฒŒ ์žˆ์–ด์„œ, ์ธ์•ฑ ํŒ๋งค (in-app purchase) ์™€ ํ•จ๊ป˜ ์ค‘์š”ํ•œ ์ˆ˜์ต์› ์ค‘ ํ•˜๋‚˜๊ฐ€ ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ชจ๋ฐ”์ผ ๋งค์ฒด๊ฐ€ ๊ด‘๊ณ  ์ˆ˜์ต์„ ์ตœ๋Œ€ํ™”ํ•˜๊ณ ์ž ํ•  ๋•Œ ๋งˆ์ฃผํ•˜๊ฒŒ ๋˜๋Š” ๋‘ ๊ฐ€์ง€ ํ•ต์‹ฌ ๊ณผ์ œ๋ฅผ ์‹ค์ฆ์ ์ธ ๋ฐ์ดํ„ฐ์™€ ํ•™์ˆ ์ ์ธ ๋ฐฉ๋ฒ•๋ก ์„ ํ†ตํ•ด ํ•ด๊ฒฐํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š”, Google AdMob, Facebook Audience Network ๋ฅผ ํฌํ•จํ•˜๋Š” ๋‹ค์ˆ˜์˜ ๊ด‘๊ณ  ๋„คํŠธ์›Œํฌ๋ฅผ ๋™์‹œ์— ์šด์˜ํ•˜์—ฌ ๋ชจ๋ฐ”์ผ ๋งค์ฒด์˜ ๊ด‘๊ณ  ์ˆ˜์ต์„ ์ตœ์ ํ™”ํ•˜๋Š” ์„œ๋น„์Šค๋ฅผ ์ œ๊ณตํ•˜๊ณ  ์žˆ๋Š” ๊ธฐ์—…, ์ฃผ์‹ํšŒ์‚ฌ ์• ๋“œ์—‘์Šค์˜ 2019๋…„ ๊ด‘๊ณ  ๊ฒฐ๊ณผ ํ†ต๊ณ„ ๋ฐ์ดํ„ฐ์—์„œ ์ถ”์ถœํ•˜์—ฌ ๋ถ„์„๊ณผ ํ‰๊ฐ€๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๋ชจ๋ฐ”์ผ ๋งค์ฒด๊ฐ€ ๊ด‘๊ณ ๋ฅผ ํ†ตํ•ด ์ˆ˜์ต์„ ์–ป๊ณ ์ž ํ•  ๋•Œ ๊ฐ€์žฅ ์ฒ˜์Œ์œผ๋กœ ๋งˆ์ฃผํ•˜๋Š” ๊ณผ์ œ๋Š”, ๋ชจ๋ฐ”์ผ ๋งค์ฒด์˜ ์„œ๋น„์Šค UX์— ์ตœ์ ํ™”๋œ ๊ด‘๊ณ  ์œ„์น˜์™€ ๊ด‘๊ณ  ํฌ๋งท์„ ๊ฒฐ์ •ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด ๊ฒฐ์ •์— ๊ฐ€์ด๋“œ๋ผ์ธ์„ ์ œ๊ณตํ•˜๊ธฐ ์œ„ํ•ด, ์ƒ๋Œ€์ ์œผ๋กœ ์ตœ๊ทผ ๋„์ž…๋œ ๋„ค์ดํ‹ฐ๋ธŒ ๊ด‘๊ณ , ๋ฆฌ์›Œ๋“œ ๋น„๋””์˜ค ๊ด‘๊ณ ๋ฅผ ํฌํ•จํ•œ ๋ชจ๋ฐ”์ผ ๊ด‘๊ณ ๊ฐ€ ๊ฐ€์ง€๋Š” ํŠน์ง•์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์ „ํ†ต์ ์ธ ๊ด‘๊ณ  ๋งค์ฒด์— ๋…ธ์ถœ๋˜๋Š” ๊ด‘๊ณ ๋ฅผ ํ†ตํ•ด ์ •์˜๋œ ๋‹ค์–‘ํ•œ ๊ด‘๊ณ  ์š”์†Œ ์™ธ์—, ์„ธ ๊ฐ€์ง€ ์‹ ๊ทœ ๊ด‘๊ณ  ์š”์†Œ; ๊ด‘๊ณ  ๋ฐ€๋„, ๋…ธ์ถœ ์œ„์น˜, ๋…ธ์ถœ ๋ฐฉ๋ฒ•์„ ์ •๋ฆฌํ•˜์˜€์œผ๋ฉฐ, ๋„์ถœ๋œ ์‹ ๊ทœ ๊ด‘๊ณ  ์š”์†Œ์™€ ๊ด‘๊ณ  ์ˆ˜์ต, ๊ด‘๊ณ  ํšจ๊ณผ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋จผ์ €, ์„œ๋น„์Šค ํ™”๋ฉด ๋‚ด์— ๊ด‘๊ณ ๊ฐ€ ์ฐจ์ง€ํ•˜๋Š” ๋น„์œจ์ธ ๊ด‘๊ณ  ๋ฐ€๋„์™€ ๊ด€๋ จํ•˜์—ฌ, ๊ด‘๊ณ  ๋ฐ€๋„๊ฐ€ ๋†’์„์ˆ˜๋ก ๊ด‘๊ณ  ์ˆ˜์ต๊ณผ ๊ด‘๊ณ  ํšจ๊ณผ, ๋ชจ๋‘ ๋†’์€ ๊ฒฐ๊ณผ๋ฅผ ์–ป์—ˆ๋‹ค. ํ•œํŽธ, ์œ ์‚ฌํ•œ ๊ด‘๊ณ  ๋ฐ€๋„๋ฅผ ๊ฐ€์ง„ ๊ด‘๊ณ  ๊ฐ„์—๋„ ๊ด‘๊ณ  ํฌ๋งท์— ๋”ฐ๋ผ ๊ด‘๊ณ  ์ˆ˜์ต, ๊ด‘๊ณ  ํšจ๊ณผ๊ฐ€ ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค. ๋‚ฎ์€ ๊ด‘๊ณ  ๋ฐ€๋„๋ฅผ ๊ฐ€์ง„ ๊ด‘๊ณ  ์ค‘์—์„œ๋Š” ๋„ค์ดํ‹ฐ๋ธŒ ๋ฐฐ๋„ˆ ๊ด‘๊ณ ๊ฐ€ ๋ฐฐ๋„ˆ ๊ด‘๊ณ  ๋ณด๋‹ค ๋” ๋†’์€ ๊ด‘๊ณ  ์ˆ˜์ต๊ณผ ๊ด‘๊ณ  ํšจ๊ณผ๋ฅผ ๋ณด์˜€์œผ๋ฉฐ, ๋†’์€ ๊ด‘๊ณ  ๋ฐ€๋„๋ฅผ ๊ฐ€์ง„ ๊ด‘๊ณ  ์ค‘์—์„œ๋Š” ๋ฆฌ์›Œ๋“œ ๋น„๋””์˜ค ๊ด‘๊ณ ๊ฐ€ ๊ฐ€์žฅ ๋†’์€ ๊ด‘๊ณ  ์ˆ˜์ต์„ ๋‚˜ํƒ€๋ƒˆ๊ณ , ์ „๋ฉด ๊ด‘๊ณ ๊ฐ€ ๊ฐ€์žฅ ๋†’์€ ๊ด‘๊ณ  ํšจ๊ณผ๋ฅผ ๋ณด์˜€๋‹ค. ๋‘๋ฒˆ์งธ ์‹ ๊ทœ ๊ด‘๊ณ  ์š”์†Œ์ธ ๋…ธ์ถœ ์œ„์น˜์™€ ๊ด€๋ จํ•˜์—ฌ, ๊ธฐ์กด PC ๋˜๋Š” ์›น ํ™˜๊ฒฝ์—์„œ๋Š” ํ™”๋ฉด ์ƒ๋‹จ์— ๋…ธ์ถœ๋œ ๊ด‘๊ณ ์˜ ๊ด‘๊ณ  ํšจ๊ณผ๊ฐ€ ๊ฐ€์žฅ ๋†’์•˜์œผ๋‚˜, ๋ชจ๋ฐ”์ผ ํ™˜๊ฒฝ์—์„œ๋Š” ํ™”๋ฉด ์•„๋ž˜์— ๋…ธ์ถœ๋œ ๊ด‘๊ณ ๊ฐ€ ๊ด‘๊ณ  ์ˆ˜์ต, ๊ด‘๊ณ  ํšจ๊ณผ, ๋ชจ๋‘ ๋” ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋…ธ์ถœ ๋ฐฉ๋ฒ• ์™€ ๊ด€๋ จํ•œ ๋ถ„์„์—์„œ๋Š”, ๋™์ผํ•œ ๋„ค์ดํ‹ฐ๋ธŒ ๊ด‘๊ณ  ํฌ๋งท์ด์ง€๋งŒ, ๋ชจ๋ฐ”์ผ ๋งค์ฒด์— ์˜ํ•ด ๊ฐœ๋ฐœ๋œ ๋…ธ์ถœ ๋ฐฉ๋ฒ•์— ๋”ฐ๋ผ, ๋ถ„๋ฆฌ๋œ ์˜์—ญ, ๋ฆฌ์ŠคํŠธ UI, Pop-up ๋กœ ๊ตฌ๋ถ„ํ•˜์˜€๊ณ , ๋‹ค์–‘ํ•œ ๋…ธ์ถœ ๋ฐฉ๋ฒ•์— ๋”ฐ๋ฅธ ๊ด‘๊ณ  ์ˆ˜์ต, ๊ด‘๊ณ  ํšจ๊ณผ๋ฅผ ๋น„๊ต ๋ถ„์„ํ•ด๋ณด์•˜๋‹ค. ๊ทธ ๊ฒฐ๊ณผ Pop-up ํ˜•ํƒœ์˜ ๋…ธ์ถœ ๋ฐฉ๋ฒ•์—์„œ ๊ฐ€์žฅ ๋†’์€ ๊ด‘๊ณ  ์ˆ˜์ต๊ณผ ๊ด‘๊ณ  ํšจ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ชจ๋ฐ”์ผ ๋งค์ฒด๊ฐ€ ๊ด‘๊ณ  ์œ„์น˜์™€ ๊ด‘๊ณ  ํฌ๋งท์„ ๊ฒฐ์ •ํ•œ ๋’ค์— ์ง๋ฉดํ•˜๋Š” ๋‘๋ฒˆ์งธ ํ•ต์‹ฌ ๊ณผ์ œ๋Š”, ๋‹ค์ˆ˜์˜ ๊ด‘๊ณ  ๋„คํŠธ์›Œํฌ๋กœ๋ถ€ํ„ฐ ๊ด‘๊ณ ๋ฅผ ์ œ๊ณต๋ฐ›์•„ ๋…ธ์ถœํ•  ๋•Œ, ๊ด‘๊ณ  ์ˆ˜์ต์ด ์ตœ๋Œ€ํ™” ๋  ์ˆ˜ ์žˆ๋„๋ก ๊ฐ ๊ด‘๊ณ  ๋„คํŠธ์›Œํฌ์˜ ์šฐ์„ ์ˆœ์œ„, ์˜ˆ์•ฝ ๊ฐ€๊ฒฉ (reserve price) ๋“ฑ ์›Œํ„ฐํด ์„ธํŒ…์„ ์ตœ์ ํ™” ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ํ•œํŽธ, ๊ด‘๊ณ  ๋„คํŠธ์›Œํฌ์™€ ๋ชจ๋ฐ”์ผ ๋งค์ฒด ์‚ฌ์ด์—๋Š” ๊ด‘๊ณ  ๋„คํŠธ์›Œํฌ๊ฐ€ ๋” ๋งŽ์€ ์ •๋ณด๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ์ •๋ณด ๋น„๋Œ€์นญ์ด ์กด์žฌํ•˜๋Š”๋ฐ, ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด๋Ÿฐ ์ •๋ณด ๋น„๋Œ€์นญ ํ•˜์—์„œ ๊ด‘๊ณ  ์ˆ˜์ต์„ ์ตœ๋Œ€ํ™” ์œ„ํ•˜์—ฌ, ์ตœ์ € ๊ฐ€๊ฒฉ (reserve price) ์ „๋žต์„ ํ†ตํ•œ ์›Œํ„ฐํด ์„ธํŒ… ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋จผ์ €, ๋ชจ๋ฐ”์ผ ๋งค์ฒด์˜ ๊ด‘๊ณ  ํŒ๋งค ๊ฐ€๊ฒฉ์ด ์ตœ์ ํ™” ๋˜์–ด ์žˆ๋Š”์ง€๋ฅผ ์„ค๋ช…ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ˆ˜์š” ๊ณก์„  ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ , ๋ฏผ๊ฐ๋„ ๋ถ„์„์„ ํ†ตํ•ด ์ œ์•ˆ๋œ ๋ชจ๋ธ์ด ๊ธฐ์กด ์šด์˜ ์ „๋žต๋ณด๋‹ค ์šฐ์ˆ˜ํ•จ์„ ๋น„๊ตํ•ด ๋ณด์˜€๋‹ค. ๋˜ํ•œ, ์ œ์•ˆ๋œ ๋ชจ๋ธ์„ ํ†ตํ•ด, ๊ด‘๊ณ  ๋„คํŠธ์›Œํฌ ๊ฐ„์˜ ์ž…์ฐฐ ๊ฐ€๊ฒฉ์ด ์ƒ๊ด€ ๊ด€๊ณ„๊ฐ€ ์žˆ์„ ๋•Œ๋ณด๋‹ค ๋…๋ฆฝ์ ์ผ ๋•Œ ๋” ๋†’์€ ๊ด‘๊ณ  ์ˆ˜์ต์„ ์–ป์„ ์ˆ˜ ์žˆ์Œ์„ ๋ฐํ˜”๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด, ํ•™์ˆ ์ ์ธ ์˜๋ฏธ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ์‹ค์ œ ๊ฒฝ์˜ ํ™˜๊ฒฝ์—์„œ ๋ชจ๋ฐ”์ผ ๋งค์ฒด๊ฐ€ ๊ด‘๊ณ  ์ˆ˜์ต์„ ์ฐฝ์ถœํ•˜๊ณ  ๊ทน๋Œ€ํ™”ํ•˜๊ธฐ ์œ„ํ•ด์„œ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ€์ด๋“œ๋ผ์ธ์„ ์ œ๊ณตํ•˜์˜€๋‹ค. ํŠนํžˆ ๊ด‘๊ณ  ๋„คํŠธ์›Œํฌ์— ๋Œ€ํ•œ ์˜์กด๋„๊ฐ€ ๋†’๊ณ , ๋‚ด๋ถ€ ์ž์›์˜ ์ œ์•ฝ์ด ์žˆ๋Š” ์ค‘์†Œ ๊ฐœ๋ฐœ์ž๋“ค์—๊ฒŒ ๋ณ„๋„์˜ R&D ์—†์ด ์ตœ์ ํ™”๋œ ๊ด‘๊ณ  ์šด์˜ ์ •์ฑ…์„ ์ˆ˜๋ฆฝํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์˜€๋‹ค.Chapter 1. Introduction 1 Chapter 2. Literature Review 11 2.1 Real-Time Bidding 11 2.2 Ad Format 15 2.2.1 Native Ads 15 2.2.2 Rewarded Video Ads 17 2.3 Advertisement Performance Index 19 Chapter 3. Evaluation of Ad Factor 23 3.1 Introduction 23 3.1.1 Advertisement Factors 26 3.1.2 Environmental Factors 29 3.1.3 Audience Factors 32 3.2 Hypotheses and Dataset 34 3.2.1 Advertisement Density 34 3.2.2 Ad Format with the Same Advertisement Density 35 3.2.3 Disclosure Position with the Same Advertisement Density 36 3.2.4 Disclosure Method of Native Ads 37 3.2.5 Dataset 38 3.3 Results 41 3.3.1 Influence of Advertisement Density on Advertising Revenue and effectiveness 41 3.3.2 Heterogenous Influence with the Same Advertisement Density 43 3.3.3 Heterogenous Influence of Disclosure Position 46 3.3.4 Heterogeneous effect by Disclosure Method 47 3.4 Discussion 49 Chapter 4. Waterfall Strategy Development 57 4.1 Introduction 57 4.1.1 Information Asymmetry 60 4.1.2 Bidding Strategy 61 4.1.3 Price and Demand 63 4.2 Estimation of Ad Networks Demand Curves 65 4.2.1 Dataset 65 4.2.2 Demand Curve Estimation 67 4.3 Waterfall Bidding Strategy 76 4.4 Sensitivity Analysis 82 Chapter 5. Conclusion 91 5.1 Summary of Research Findings 91 5.2 Contribution of this Study 94 5.3 Limitation and Further Studies 96 Bibliography 97 Appendix 109 Abstract (Korean) 121Docto

    Defining Markets That Involve Multi-Sided Platform Businesses: An Empirical Framework With an Application to Google's Purchase of DoubleClick

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    A multi-sided platform (MSP) serves as an intermediary for two or more groups of customers who are linked by indirect network effects. Recent research has found that MSPs are significant in many industries and that some standard economic results, such as the Lerner Index, do not apply to them, in material ways, without some significant modification to take linkages between the multiple sides into account. This article extends several key tools used for the analysis of mergers to situations in which one or more of the suppliers are MSPs. It shows that the application of traditional tools to mergers involving MSPs results in biases the direction of which depends on the particular tool being used and other conditions. It also extends these tools to the analysis of the merger of MSPs. The techniques are illustrated with an application to an acquisition by Google in the online advertising industry.

    Pricing average price advertising options when underlying spot market prices are discontinuous

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    Advertising options have been recently studied as a special type of guaranteed contracts in online advertising, which are an alternative sales mechanism to real-time auctions. An advertising option is a contract which gives its buyer a right but not obligation to enter into transactions to purchase page views or link clicks at one or multiple pre-specified prices in a specific future period. Different from typical guaranteed contracts, the option buyer pays a lower upfront fee but can have greater flexibility and more control of advertising. Many studies on advertising options so far have been restricted to the situations where the option payoff is determined by the underlying spot market price at a specific time point and the price evolution over time is assumed to be continuous. The former leads to a biased calculation of option payoff and the latter is invalid empirically for many online advertising slots. This paper addresses these two limitations by proposing a new advertising option pricing framework. First, the option payoff is calculated based on an average price over a specific future period. Therefore, the option becomes path-dependent. The average price is measured by the power mean, which contains several existing option payoff functions as its special cases. Second, jump-diffusion stochastic models are used to describe the movement of the underlying spot market price, which incorporate several important statistical properties including jumps and spikes, non-normality, and absence of autocorrelations. A general option pricing algorithm is obtained based on Monte Carlo simulation. In addition, an explicit pricing formula is derived for the case when the option payoff is based on the geometric mean. This pricing formula is also a generalized version of several other option pricing models discussed in related studies.Comment: IEEE Transactions on Knowledge and Data Engineering, 201

    Glosarium Keuangan

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    Machine Learning for Ad Publishers in Real Time Bidding

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    Practical Strategic Reasoning with Applications in Market Games.

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    Strategic reasoning is part of our everyday lives: we negotiate prices, bid in auctions, write contracts, and play games. We choose actions in these scenarios based on our preferences, and our beliefs about preferences of the other participants. Game theory provides a rich mathematical framework through which we can reason about the influence of these preferences. Clever abstractions allow us to predict the outcome of complex agent interactions, however, as the scenarios we model increase in complexity, the abstractions we use to enable classical game-theoretic analysis lose fidelity. In empirical game-theoretic analysis, we construct game models using empirical sources of knowledgeโ€”such as high-fidelity simulation. However, utilizing empirical knowledge introduces a host of different computational and statistical problems. I investigate five main research problems that focus on efficient selection, estimation, and analysis of empirical game models. I introduce a flexible modeling approach, where we may construct multiple game-theoretic models from the same set of observations. I propose a principled methodology for comparing empirical game models and a family of algorithms that select a model from a set of candidates. I develop algorithms for normal-form games that efficiently identify formationsโ€”sets of strategies that are closed under a (correlated) best-response correspondence. This aids in problems, such as finding Nash equilibria, that are key to analysis but hard to solve. I investigate policies for sequentially determining profiles to simulate, when constrained by a budget for simulation. Efficient policies allow modelers to analyze complex scenarios by evaluating a subset of the profiles. The policies I introduce outperform the existing policies in experiments. I establish a principled methodology for evaluating strategies given an empirical game model. I employ this methodology in two case studies of market scenarios: first, a case study in supply chain management from the perspective of a strategy designer; then, a case study in Internet ad auctions from the perspective of a mechanism designer. As part of the latter analysis, I develop an ad-auctions scenario that captures several key strategic issues in this domain for the first time.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75848/1/prjordan_1.pd

    Essays In Algorithmic Market Design Under Social Constraints

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    Rapid technological advances over the past few decades---in particular, the rise of the internet---has significantly reshaped and expanded the meaning of our everyday social activities, including our interactions with our social circle, the media, and our political and economic activities This dissertation aims to tackle some of the unique societal challenges underlying the design of automated online platforms that interact with people and organizations---namely, those imposed by legal, ethical, and strategic considerations. I narrow down attention to fairness considerations, learning with repeated trials, and competition for market share. In each case, I investigate the broad issue in a particular context (i.e. online market), and present the solution my research offers to the problem in that application. Addressing interdisciplinary problems, such as the ones in this dissertation, requires drawing ideas and techniques from various disciplines, including theoretical computer science, microeconomics, and applied statistics. The research presented here utilizes a combination of theoretical and data analysis tools to shed light on some of the key challenges in designing algorithms for today\u27s online markets, including crowdsourcing and labor markets, online advertising, and social networks among others
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